FROM BUSINESS MODEL TO BUSINESS MODEL PORTFOLIO IN THE EUROPEAN BIOPHARMACEUTICAL INDUSTRY

Keywords: Biopharmaceutical, portfolio, corporate strategy, business strategy, core competence, coherence, value chain

At the crossroad of firm's core competencies and of the anticipations of consumers' needs, the business model approach complements corporate and business strategy approaches. Firms combine several business models simultaneously to deliver value to different markets, building a portfolio of business model. For managers, business model and business model portfolio are particularly useful to address customer's needs and organisational capabilities of the firm. They also emphasise how the initial core competency of the firm can be extended or redeployed to increase the rent. Business model portfolio describes the firm's strategy to balance time-to-market, revenue stream, risk and interdependencies. It conceptualises firm diversification within the same industry to generate and capture rents. They finally describe two generic dimensions: core competence extension to enlarge the market and to address additional customers and core competence redeployment to serve similar market with the same core competence.

INTRODUCTION

Original business models emergence in new industries and become rapidly iconic.

Those of firms that have shaped the biotech industry (such as Millennium, Celera Genomics or Genentech) revolutionised the environment by inventing unprecedented ways to bridge upstream research, venture capitalists and large firms. However the most successful biotech companies have generated revenues by implementing several different business models simultaneously to serve different customers. But the portfolio of business models underlying these success stories have been generally hidden by the most emblematic model. Many firms manage a portfolio of business models to develop the market value of their core competencies and to generate revenue streams to balance time-to-market pressure, risks, interdependency and expected returns.

The article examines the biopharmaceutical industry (i.e. pharmaceutical and biotechnology companies) to analyse how biotech firms bridge core competencies and business strategies to develop business models. It argues that firms can combine different business models to deliver value simultaneously to different markets. Teece [START_REF] Teece | Business model, business strategy and Innovation[END_REF] defines a business model as the 'way in which the business enterprise delivers value to customers, entices customers to pay for value, and convert those payments to profit', and we define a portfolio of business models as the range of different ways a firm delivers value to its customers to ensure both its medium term viability and future development. Using four in-depth case studies we explore how biotech firms use different business model designs to generate added value and profit from their core competencies, and balance time-to-market, risks, interdependency and expected returns to create medium term value and longer term viability by organising and combine these models at the firm's corporate business architecture level.

The article contributes to current learning in three ways: first of all, it positions the notion of 'Business model' as an intermediary concept bridging firms' core competencies and business strategy. Second, it introduces and discusses the notion of a portfolio of business models as a way to articulate and finance the firm's activities in the medium run and to ensure idiosyncrasy to protect its future health. Third, it analyses how young firms manage to build simultaneously a short term business and invest into their future business.

The first part of the article explores the relationship between core competencies and business models, i.e. ways to create and capture value based on core competencies.

The data and methodology section then describes the four case studies of firms and their six business models, while the third section discusses management of a business model portfolio.

FROM BUSINESS MODEL TO BUSINESS MODEL PORTFOLIO

Fab-less, e-commerce, commercialisation of science, two sided markets: these new labels describe new ways to organise activities within firms and to reposition businesses to add value for customers and to generate profit for the firm. All firms implement business models, and many implement several simultaneously to deliver value for different categories of consumers and to balance revenue stream, risks and interdependencies with other actors. At the corporate level, firms may manage such portfolios of business models to generate value from their core competencies to address different customers. We first discuss the notion of the business model, considering its two fundamental dimensions: as a concept and as a tool. Business models are then characterised by four criteria (time-to-market, risk, interdependencies and expected returns). The section then introduces the concept of the business model portfolio as combining these four criteria to provide the firm with a sustainable revenue stream.

The Business Model -a Bridge between Business Strategy and Core

Competences

The concept of business model is an intermediary concept between core competencies and business strategy, linking internal appraisal of organisational capabilities and the strategic positioning on evolving markets. Core competencies (or core capabilities) refer to a firm's fundamental ability for undertaking specific activity, to create a value proposition for the ultimate customer or to deliver that value more efficiently. Hamel and Prahalad [START_REF] Prahalad | The core competence of the corporation[END_REF] argue that capabilities must be appraised in comparison with those of other firms, while Leonard-Barton [START_REF] Leonard-Barton | Core Capabilities and Core Rigidities : A Paradox in Managing New Product Development[END_REF] defines capabilities as core 'if they differentiate a company strategically'. By articulating core competencies and business model, strategists understand how firms benefit from their uniqueness and generate profit by delivering value to customers and transforming value into profit. Business model connects core competencies with market and customers. A firm's core competences condition its range of possible activities, because it determines which pathways can be opened where it can add value. They are the basis of its business logic, which is then deployed in terms of activities to build up a business model. Business model and business strategy are deeply interrelated. Teece [START_REF] Teece | Business model, business strategy and Innovation[END_REF] describes business model as 'deep truths' about customers and their wants and needs. The entrepreneurs figure out what customers want and ways to satisfy them, by articulating those needs and matching them with technological possibilities and relevant organisation. Business strategy is a key component of the competitive strategy analysis which intends to isolate mechanisms to maintain the competitive advantage [START_REF] Chesbrough | The Role of Business Model in capturing Value from Innovation: Evidences from Xerocs Corporation's Technology spin-off companies[END_REF] and idiosyncratic nature of firm activity, while business model focuses on a specific activity. As business strategy, business model does not cover the entire company, and firms may be involved exploiting several business models. The concept of business model covers not only the firm's interaction with the customers but also how it organises to benefit from the value delivered. Business model bridges the internal appraisal of core capabilities to understand how the firm builds its uniqueness in the combination of resources and business strategy, which studies how firms create and maintain competitive advantage. The concept of business model articulates the creation and value delivering for the customer, with the organisational capabilities of the firm to differentiate strategies [START_REF] Magretta | Why Business Models Matter[END_REF].

The Business Model as Practical Tool for Managers

Morgan and Baden-Fuller [START_REF] Morgan | Business model: Theory and Practices[END_REF] argue that business models are not only a concept but also a practical tool for managers. As in economics, models are used in management to make up for the lack of knowledge, as a tool to inquire into how firms work, and to figure out relationships between their different elements so as to imagine how they might work. Business models illustrate relations between value creation and value capture activities for strategists, questioning existing value chains, and articulating possible opportunities for core competencies within value chains. This can be particularly valuable when industries change rapidly. As an industry matures, its value chains can be profitably de-integrated, allowing greater specialization of inputs and outputs and leading to improved efficiency and greater speed to market [START_REF] Sabel | How to keep mature industries innovative[END_REF], and Schweitzer [START_REF] Schweizer | Concept and Evolution of Business Models[END_REF] has identified this disintegration of the value chain as offering opportunities for new business models. Norman and Ramirez [START_REF] Normann | From Value Chain to Value Constellation: Designing Interactive Strategy[END_REF] also propose that exploding the rigid value chain into a constellation of actors can allow it to be reconfigured; generating new ways to create value and leading to contestable markets [START_REF] Baumol | Contestable Markets: An Uprising in the Theory of Industral Structure[END_REF]. Studying the origins of the biotech industry, McKelvey [START_REF] Mckelvey | Evolutionary Innovations : The Business of Biotechnology[END_REF] considered the early stage strategy of Genentech, pointing out the de-integration of the value chain in the pharmaceutical industry during the 80s. The introduction of biotechnologies has opened up fresh opportunities for new entrants to propose alternative business models, to differentiate their offerings and to deliver added value to customers.

The first category of the Business Model as a tool deals with the production of newness, technological innovation or in ways of doing business. New technologies may require new business models to capture value, as in the biotech industry where scientists have been able to create value from the production of scientific results, extending the existing pharmaceutical industry value chain backwards towards basic scientific research. Thus the business model can be a leverage tool to question the existing value chain and to identify (new) high added value positions in the value chain. Or the 'newness' may involve new ways to deliver existing technologies: the advent of the Internet has changed the existing value chain of computer production and resellers by creating direct communication between producers and consumers [START_REF] Magretta | The power of virtual integration: an interview with Dell Computer's Michael Dell[END_REF], as was the case, for example, with Dell, when direct internet selling enabled Dell to both enhance the value proposition for its consumers and the value capture opportunities for the firm.

The second category of tool deals, with the dilemma of rent creation and appropriation. Different actors or firms are involved in the value creation process, all of which seek to generate rents from the value created which, (as Pisano [START_REF] Pisano | Can biotech be a business[END_REF] and Durand et al. [START_REF] Durand | Do Science and Money Go Together? The Case of the French Biotech Industry[END_REF] point out) inevitably generates tensions. In the biopharmaceutical industry, where firms develop new drugs and new techniques based on upstream research, those closer to the markets and to customers are better positioned to capture rents. Most of biotech SMEs have not been able to capture the value they anticipated, and rent appropriation levels are lower than expected. As business models focus on how to deliver value to markets, they can allow the firm to differentiate their strategic value creation and rent appropriation action. When markets seem incontestable, incumbents dominate the competition and value-chain positions seem to be 'locked', thinking in terms of business models offers a way to re-establish contestability by inventing new ways to compete, be they alternative ways to deliver products or services, or by developing new technological innovations or adopting new positions in the value chain.

Four Criteria to Characterise Business Models

Strategists and strategic authors have tended to characterized business models by pointing out 'polar' positions: service oriented firms with short time to market and low expected returns versus product oriented firms with longer times to market and high expected returns; [START_REF] Mangematin | Sectoral system of innovation, SMEs development and heterogeneity of trajectories[END_REF] [START_REF] Casper | How do technology clusters emerge and become sustainable? Social network formation and inter-firm mobility within the San Diego biotechnology cluster Research Policy[END_REF], alliances versus final consumers [START_REF] Niosi | Alliances are not enough. Explaining rapid growth in biotechnology firms[END_REF]; commercialization of science as intermediary services versus commercialization of products; direct on-line commercialization versus shops [START_REF] Osterwalder | Clarifying Business Models: Origins, Present, and Future of the Concept[END_REF] [19], Chesbrough and Rosenbloom [START_REF] Chesbrough | The Role of Business Model in capturing Value from Innovation: Evidences from Xerocs Corporation's Technology spin-off companies[END_REF] and Voelpel et al. [START_REF] Voelpel | The Wheel of business model reinvention: how to reshape your business model to leagfrog competitors[END_REF]. However, when we review the list of criteria used to characterise business models, we can identify four dimensions as synthesizing the previous works on high tech business models: time to market, technological/financial risks, interdependencies amongst actors and expected returns.

Time to market characterises how and when a product or service reaches the market.

It influences the business logic and is one of the primary factors in establishing competitive edge [START_REF] Messica | Time-to-Market, window of opportunity and salvageability of a new product Managerial and Decision[END_REF]. Strong market pressures tend to force firms to reduce their R&D time horizons to lessen their time to market [START_REF] Dunk | Short-term R&D bias, competition on cost rather than innovation, and time to market[END_REF]. Time-to-market also determines the firm's ability to finance its day-to-day activities before cash can be generated. Cash is burnt before product or service launch to market: if times-tomarket are long, firms' activities must be funded by their other activities, or by venture capital. Time-to-market also affects appropriation levels -investors financing firms involved in R&D will appropriate part of the eventual rents as their returns, and their slice of the action will increase the longer the product or service takes to come to market.

Risks also affect the firm's business logic, and have both technological and financial dimensions [START_REF] Blomkvist | Psychological aspects of values and risks[END_REF]. The more radical the innovation, the more the firms runs the risk that, in the end, it will have nothing to take to market that time, energy and reputation will have been wasted and there will be nothing to appropriate from which to fund future activity, or to repay investors. In the case of the biopharmaceutical industry the technological dimension also concerns drug failure: drug may fail after early or advanced clinical trials. Risks are also closely linked with the time frame required for ramp-up -longer timescales heighten both dimensions of risk.

Interdependencies with other organisations are related to the levels of specificity of the firm activity. Following Afuah and Utterback [START_REF] Afuah | Responding to Structural Industry Changes: A Technological Evolution Perspective[END_REF] work about industry life cycle, the power of suppliers is relatively low during the emergence phase as specific assets have been developed. As the market increases and firm specificity grows, its relations with its suppliers and producers (and even consumers) will increase, and it will adopt strategic behaviour and adapt its business model to manage these interdependencies.

When interdependencies are high and the firm needs additional competencies to perform its activity, alliances may be formed, complicating both its control of the activity and its ability to appropriate rents. When its exchanges with other actors remain non-specific, interdependencies are lower and the firm is better position to appropriate the generated rent.

Expected Returns are the anticipated level of value generated by the activity, and the promises made by the firm to its shareholders in terms of the return they can expect on their investments. The level of the firm's returns will depend upon the size of the targeted market, the amount of investment and the degree of its success in appropriating rents.

Any business model can be characterised on these four dimensions, which are all interrelated. The drug development business model requires usually long investment before the launch on the market; it is highly risky and often requires firms to ally to finance and pursue their activities, and such development activity is only possible because expected returns are very high. At the opposite end of the scale, a service business model is characterised by shorter time to market, lower investment needs and smaller expected returns, which can enable the firm to carry through development internally. At the corporate level, firms often develop several business models simultaneously to balance these four characteristics and run multiple activities over on the medium term, the returns from some financing the others. To analyse how firms organise their activities at the corporate level, we develop the concept of business model portfolio building on the literature about strategic business unit portfolios (Boston Consulting Group matrix, McKinsey matrix, Ashridge Portfolio Display, etc.) and on the management of risks in corporate innovation [START_REF] Berglund | Risk conception and risk management in corporate innovation: lessons from two swedish cases[END_REF].

Business Model Evolution in the Biopharmaceutical Industry

The biopharmaceutical industry has been very inventive in terms of business models.

The biotech industry has been the setting for much development in the commercialisation of science, leading to the increasing role of university patenting [START_REF] Mowery | The Growth of Patenting and Licensing by U.S. Universities: An Assessment of the Effect of the Bayh-Dole Act of 1980[END_REF]. and has also seen value chain de-integration opening up many opportunities for developing new activities.

But it is also one where promises have been so high for so long, but where results have remained far below expectations [START_REF] Pisano | Can biotech be a business[END_REF][START_REF] Hopkins | The myth of the biotech revolution: An assessment of technological,clinical and organisational change[END_REF]. Firms' managers -and the industry as a whole -promised to shorten drug development process, from heuristic to rationale drug development. Biotechnology promised personal medicine and affordable drugs for patients, personal genetic diagnostics and increased prevention capabilities for genetic diseases, and big returns on investments for biotech firms and investors. But after almost thirty years, Pisano has shown how the industry's structure is flawed: 'islands of expertise' need to be brought together to develop a medicine; while scientific knowledge is being created, new drugs discovered and process development improved, appropriating rents remains problematic [START_REF] Durand | Do Science and Money Go Together? The Case of the French Biotech Industry[END_REF]; and despite all the industry's explorations and experiments with new business models, none has yet appeared as a dominant design.

In the modern sense of biotechnology, the industry appeared in the mid 1970s as the development of genetic engineering and monoclonal antibodies disrupted the traditional drug development process that was essentially based on the synthetic organic chemistry heuristic [START_REF] Hopkins | The myth of the biotech revolution: An assessment of technological,clinical and organisational change[END_REF]. The core competences of pharmaceutical firms were their accumulated know-how and their access to patients to perform trials. The first However, since 2000, the value chain has been de-integrated, and a new form of networked industrial structure involving highly specialised actors is emerging [START_REF] Rothman | Downstream and into deep biology: Evolving business models in 'top tier' genomics companies[END_REF].

Large pharmaceutical firms fill gaps in their R&D pipelines and research portfolios by collaborating with smaller firms, buying or co-developing drugs at any stage of development [START_REF] Hopkins | The myth of the biotech revolution: An assessment of technological,clinical and organisational change[END_REF]. And biotechnology developments have focused not just on products, but also on specific and high value added service elements.

DATA COLLECTION AND ANALYSES

This section describes the generic core biotech industry competencies and presents two levels of analysis -the business model and the firm. Case studies of European biotech firms and their business models are examined to analyse the business models firms employ to put their core competencies into action to deliver value, so as to illustrate how they build business model portfolios at the corporate level. Data collection was organised to gather data at two levels: business models and firms.

First, sixteen experts of the European biopharmaceutical industry were interviewed to map out the core competencies along the drug development value chain and understand the basis underlying today's business models. Second we conduct four case studies of European biopharmaceutical SMEs to gain fine grained analysis of business models portfolio, showing how young companies employ different models to optimise the allocation of their resources.

Three Core Competencies

Experts have identified three core competencies in the contemporary biotech industry (for more details see appendices 1 and 2):



The traditional competence of drug discovery, which follows the conventional drug development route illustrated above, focuses primarily on the process new drug development by creating molecules with the potential to be developed as therapeutic agents, and is the basis of every business model focused on the discovery and ownership of candidate drugs;  The architecture and recombination of knowledge from the biotech and biopharmaceutical industries, in which firms reorganise their knowledge to detect opportunities aiming to mobilise, combine or benefit from external knowledge. This competence focuses on the ability of the firm to orchestrate knowledge from its partners rather than on its own ability to develop new drugs, and may involve value chain reconfiguration;  The processes optimization competence. As biotechnolgies matures, costs become more significant, and the more efficient industrialisation of services, R&D or production processes, or the improvement of linkages between the two, become more important.

Table 1 evaluates the characteristics of the different business models based on these core competencies (for more details about the evaluation of the characteristics see appendix 3). The drug discovery core competence is characterised by a long time needed to work through the development route value chain, levels of risk and interdependency are high, because outcomes cannot be guaranteed but high levels of financial investments will be required whether the searches end in success or failure and the focal firm will need external expertise -but expected returns can be very high: launching a successful new drugs onto the market will yield allow good returns and margins. The knowledge architecture core competence involves medium time to market and risks, and while interdependency with other firms is high, so are the expected returns. The process optimisation core competence generally involves short times to market with low or medium risks and requires few interdependencies, but does not promise high returns. Based on each core competence, firms adapt, design and amend business model to improve their characteristics: reducing time-to-market for drug discovery, managing interdependencies and risks for knowledge architecture more effectively and increasing the expected returns to process optimisation.

Business Models at the Firm Level

To describe business models and they are enacted within a firm, we focus on four case studies. As the post-2000 period has been characterised by the increasing specialization of business models, we limited our sample to firms founded since 2000. We employed the technique of searching for polar types, where an important guideline is to select cases that contradict patterns noted in prior cases study [START_REF] Pettigrew | Longitunal Field Research on change : theory and practice[END_REF]. So we sought companies with business models that differed substantially from the fully integrated pharmaceutical company business model [START_REF] Rothman | Downstream and into deep biology: Evolving business models in 'top tier' genomics companies[END_REF] that has been studied often.

Four European biopharmaceutical companies suited these criteria and agreed to be part of the study.

Data collection began with meeting the founders of the company and questioning them about the company's history, its activities and markets. Each interview lasts at least two hours and generated an overall view of the company's bundle of activities.

(A high level of confidentiality was assured to secure access to strategic information.)

The second phase was dedicated to analysing documents and cross-referencing internal sources against information from specialized press and website sources. The third phase comprised in-depth individual interviews lasting an hour and half with the CEO, chief scientific officer (CSO) and chief financial officer (CFO) of each firm, focusing on the financing model, the structure of costs, investments, scientific challenges, times to market and the revenue model. We also asked about the firm's partners and their levels of involvement, and about intellectual property issues around the firm's products or services, parameters which help us determine the company's business models and the way its portfolio is managed.

We first analysed core competences in action i.e. how firms designed effective business models to match customer expectations, looking at how the firm adapted its business model based on the three generic core competences as well as the firm's positioning on the value chain.

The second level of analysis was of the firm as a whole, to discover how it combined its different business models to implement its overall strategy. Three of the four firms were managing a portfolio of different business models, while the other replicated the same business model in order to maximise its value proposition: we study how each enacts its business models to generate and appropriate rents.

Analyses of Four Cases

OphSmart is a small company of thirty people founded in 2005 which develops a pipeline of products all focusing the ophthalmology therapeutic domain. OphSmart has two core competencies: drug discovery and knowledge architecture. In the drug discovery realm, OphSmart specialises in upstream collaborations with universities to renew its own pipeline. In terms of knowledge architecture, OphSmart seeks to shorten drug development times by repositioning drug candidates in ophthalmology.

These drugs are bought from other companies. Then OphSmart manages a network of firms to develop the molecules: it does not invest in manufacturing or laboratories, OphSmart externalises every step of the drug development. It therefore runs three interrelated business models concurrently: collaboration with universities to discover molecules; repurposing of existing molecules to its therapeutic domains and organising networks to form a virtual company. Each time, the business logic is based on a dense network of contacts, built up over the projects and over the previous deep involvement in the industry of the management team.

Emics is a small company run by a team of three people which, since its founding in 2003, has specialised in one therapeutic domain (a worldwide infectious disease) and is mainly focused on developing two vaccines that originated in the firm's in-house scientific collaboration with an academic laboratory. Emics mobilises its core competencies successively: drug discovery to identify new potential vaccines, and knowledge architecture to orchestrate its network of partners and suppliers to develop its vaccines, arranging its partners' different competencies to form a virtual vaccine company. The analysis of the four companies and of their six business models shows how firms build on their core competencies and organize their activities to generate rents. The combination of business models within portfolio balance the firm activities and its revenue streams.

OpteX

ADAPTING BUSINESS MODELS AROUND THREE CORE COMPETENCIES

The relationships between core competencies and business models are discussed first, followed by descriptions of how firms organize their business model portfolio.

Drug Discovery Core Competence

Such firms share risks with partners and aim to shorter the time to market by using business models different to the traditional business model for drug development.

Collaboration for Discovery model: to deal with the high risk/high time-to-market drug discovery model, OphSmart and Emics develop alternative sourcing of innovation by collaborate with universities and academic laboratories or other companies in long term research collaborations to share the risk and speed up the process of carrying out extreme upstream research to develop new drugs. They limit their investment and risk by specialising in upstream research and early discovery phases to, and sign exclusive licences with their discovery partners to appropriate value. Time to market is shortened and investments are recouped by selling on drug candidates to partners lower in the value chain. Although the risk of drug failure remains, partnering allows firms to lower its own risk for the company.

Interdependency is high -as the partner is a key to accessing knowledge -but expected returns, based on drug selling, are high.

Knowledge Architecture Core Competence

Firms change the architecture of knowledge in the biotech industry. All these activities are based on knowledge architecture core competencies. They are undertaken by small firms with highly skilled and well informed scientists who are able both to combine different technologies, therapeutics and also to negotiate agreements with CEOs: Emics manages the development of two vaccines with only three people; OphSmart manages four repurposed drugs and four drug candidates with thirty people, Betwin manages three product with a team of five; OpteX management team connect previous clients and partners. Activities are performed at the margins and accessing the value chain through partners, sourcing innovation from other firms or connecting companies together to make deals. In these business models, firms articulate knowledge in original ways to produce new drugs or to offer new services that are unique on the market. Comparing to the traditional business model of drug discovery these business models reduce time to market and risks: managing an array of partners brings expertise at the very needed moment and reduces investments confronting to the fully integrated pharmaceutical company.

Process Optimization Core Competence

The core competence of process optimisation deals with optimising drug development processes. R&D or production processes can be made more efficient, or linkages between the two improved. Optimisation concerns services, research tools or production. In our firm we found two business models built on this core competence. Activities in business models based on process optimisation are not core to the main value chain, but provide services for value chain actors. Firms provide the same kind of actions for any product, corresponding to the 'layer player' archetype [START_REF] Schweizer | Concept and Evolution of Business Models[END_REF]. With shorts time to market these business models generate cash in the short term.

Table 2 presents the adapted business models based on the three core competencies in the biotech industries and observed in practice. Each of the business models improves at least one of the criteria: reducing risk for collaboration in discovery, maintain high returns with limited risks for business models based on knowledge architecture and shortening time to market for optimisation business models. All the firms have developed different activities within the same industry, organising their business models to align their organisational capabilities to address specific customers and to extract added value from their core competencies.

BALANCING A BUSINESS MODEL PORTFOLIO

In practice these four small European biopharmaceutical companies may use more than one competence to position themselves on the value chain and may thus employ several business models. The business architectures of OphSmart, Emics and OpteX involve managing several business models simultaneously, with two core competencies in each case. A key issue for these companies is to try to moderate time to market and risks in order to attract investors, redeploying or extend the same business logic towards additional costumers. Betwin is the only one which reuses the same core competence into different markets.

OphSmart's portfolio mixes long and medium times to market, medium and low risks, high and low needs for alliances and high expected returns. Its three business models enable it to balance the time to market, risks and interdependency to keep returns on investment high. Its activities are also linked: whether a molecule is repurposed or discovered in-house, it will be developed via the virtual development business model. OphSmart redeploys its core competencies to capture value at different stages of the value chain. Emics runs two business models based on one core competence each, its two core competences, allowing it to balance time to market and risks because, although these two criteria should be high (as in its discovery-based business model), they are lowered by its knowledge architecture competence. OpteX's business models do not promise high returns, but times to market are short and risks low, allowing it to generate short-term rents successfully.

OpteX originally used its core process optimisation competence to manage two business models that were internally coherent -the technology platform and contract manufacturing organization (CMO) activities followed the same sequential drug development steps -and re based on the same production optimisation business logic. But the firm also detected the opportunity of technology brokering on the value chain using the strong network of clients developed via its original business modelsthey naturally became both sources and customers for its new technology brokering model, with OpteX acting as a link between them. OpteX was able to align this new activity coherently both with its business sector and with its current activities. Once its three business models are stabilized the company will be able to take more codevelopment risks in its technology platform business model.

Finally, Betwin has one business model which it has replicated continuously, which, because it focuses on new opportunities created by intermediary markets, assures it of a medium time to market with medium risks and promises high returns to investment.

Betwin's founders recognised this new opportunity on the value chain, and adapted their business logic specifically to match this profit zone. Betwin has developed capabilities to detect molecules and to organise networks to deliver them. So the firm is organized on a virtual business model to buy early developed molecules to create value for its customers.

These four companies show that business models can be balanced to insure short or medium time-to-market, which is important as it is difficult for small companies to survive the long period -of maybe twelve years -without turnover and profit involved in longer-term models. Long time lags between investment and the first generation of turnover can often lead to rejection by investors, whereas being able to promise mid-term rents can reassure shareholders that they will see a return on their investment within five years.

Table 3 sums up each firm's business model portfolio, and emphasises the relationships between core competencies and business models and different ways to address the same market. Firms invest in different business models to limit the level of risk and to speed up time-to-market for part of their activities. Firms with drug discovery as their core competence need to benefit from additional competencies to develop new drugs, so they are often involved in dense networks to perform their activities, and thus need to be able to manage interdependencies effectively. For firms investing in knowledge architecture as a core competence, their activity is to manage linkages with other actors, and they are thus also highly interdependent. Finally, firms that invest in optimisation, and whose business is to optimise other actors' production processes, so they, too, depend on others. These business model portfolios portray an interesting link between with risk and time-to-market: while product companies promise a high value, alternative business models are used to balance risk and time-to-market values.

So (compared to fully integrated pharmaceutical companies) these small firms have found ways to promise high value, medium risk and acceptable times-to-market.

DISCUSSION

A business model can be defined as standing at the intersection between management expectations about market needs and how they design their activities and organise their capabilities to meet them, by linking core competencies and markets. A business model portfolio refers to a range of different activities allowing a firm to meet different consumers' needs and build the idiosyncrasy of the firm's bundle of activities. The concept of business model portfolio to balance risk, revenue stream and interdependencies is discussed, as positioned between business strategy and corporate strategy.

Strategies with Business Model Portfolio

Business models bridge management's expectations about customer needs and their core competencies. In maturing industry, specialisation and division of work within the value chain create opportunities for firms to design new business models and new value propositions. This article argues that a core competence can be transformed in several business models, and shows how, in the biopharmaceutical industry, firms have found numerous ways to translate their core competencies into activities to address costumers at different stages of the value chain. The business model as a concept linking core competencies, the value chain and consumers' needs can help promote strategic thinking from both the internal and external perspectives.

The existence of a portfolio of business models reveals the firm's maturity as it develops from the start-up phase (based on a single business model) to managing a more complex architecture designed to reduce risks and maintain equilibrium across a variety of activities. Case studies reveal that firms tend to add activities that relate to some aspect of their existing ventures, either based on the same core competence or developed to take exploit certain common technological and market characteristics. Firms can have one or more core competences enacted within different business models, reveal the coherence of their activities [START_REF] Teece | Understanding Corporate Coherence : Theory and Evidence[END_REF]. Different business models can be driven by similar business logics, when they extend the market niche addressed or the services or products offered to similar audiences.

Companies may run parallel, vertical integrated business models to cover more sectors of their value chain, and their consecutive articulation can allow the firm to benefit from synergistic effects, adding more value for customers and (hopefully) more profit for the firm.

Table 3 presents the different strategic positions in portfolio of business model. The two main dimensions to deal with are risks and interrelatedness between business models, which deeply affect the organisation of the activities. On the one side, it aggregates technological and financial risks as well as time to market; on the other side it assesses the degree of relatedness between existing activities within the firm. Similarly, business model portfolio (BMP) is not equivalent to business strategy which positions the firm activities on different industries, according to their relative attractiveness. BMP can be developed within the same range of activities, by extending similar business logics to additional market niches, by serving similar customers elsewhere along the value chain or by capturing rents on existing activities through the transformation of distribution channels.

The need to understand business models and business model portfolios -as concepts and a tool -will intensify as start-ups and small firms increasingly become the cornerstones of economic development. Traditional strategic analysis, expressed in terms of business and corporate strategy, is poorly adapted to small ventures which are involved in one industry: business models can help fill the knowledge gap about small venture strategies. Focusing on how the firm's organisational capabilities can be designed to address costumer needs, the concept of business model covers the coherence of activities between internal organisation and markets, and can be a way to emphasise how the initial activity of the firm can be extended or redeployed to increase the firm's rents. A business model portfolio encapsulates the firm's strategy to balance multiple activities, risks and revenue streams. It conceptualises firm diversification within the same industry to generate and capture rents and to balance the times-to-market of different products or services. Finally it illustrates two generic strategies: core competence extension to enlarge the market and to address additional customers and core competence redeployment to serve similar market with the same core competence.

CONCLUSION

A business model can be seen as an intermediary tool between corporate and business strategies, and defined as articulating a value proposition for an evolving value chain, and the way in which the firm delivers product or services to create and then appropriate value. A firm's business strategy determines how it uses its resources, satisfies its long-run objectives, and defines the implementation of its corporate strategy [START_REF] Grant | Contemporary Strategy Analysis[END_REF]. The business model articulates both the internal and external dimensions, bridging between capabilities and competences, and value chains, markets and customers, focusing on the coherence of activity within both the industry and the firm.

A biopharmaceutical company providing R&D services and production of early clinical trials will segment its business strategy depending on the market: e.g. proteins vs. monoclonal antibodies. But the business model view considers two business models: the contract research organization and the contract manufacturing organization. Although the markets are different, the activities are closely linked and the company will enhance its coherence and synergy effects if it manages its business models to benefit from these effects.

For managers, the concepts of business models and business model portfolios are particularly useful to understand that firms can pursue multiple business models within one strategy. This meso-level analysis generates descriptions that allow strategists to better position the firm activities. In addition, industry matures, and its activities, actors and their relationship are continually evolving. To put it simply, the notion of the business model which articulates activities within the firm and its relation with customers is particularly suited to changing environment. Different ways may be explored, from core competence extension to duplication in other fields.

For a firm that is deeply embedded in networks, when its frontiers are becoming blurred and its value chain shifting, the notion of the business model can help improve the coherence of its resource and capability allocations between activities. It drives an analysis of the firm that can allow it to adjust its position on the value chain and its mechanisms for value creation and capture. Managers who initially developed the concept of the business model were searching for a strategic tool that could take into account the problems of how to address several markets with a pertinent value propositions, and how to develop a flexible architecture to do business in complex environment. In fast changing and fast growing industries, value chains can get continually reconfigured, heralding the appearance of new zones of profit. With a business model portfolio a company can easily construct and reorganize its activities in coherence with both the firm and with the evolving environment. Managing a business model portfolio helps piloting time-to-market, risks, interdependency with other firms and expected returns, and can give clear indications of synergies that exist -or that should or might exist -between its different activities.

Annexe 1: Interviewees

Respondent 10

European biotechnology medium company CEO manages a biopharmaceutical company dedicated to the discovery and development of products innovation for a specific disease.

Respondent 11

European biotechnology medium company CEO who manages a biopharmaceutical company founded in 1990 dedicated to the development of vaccines against infectious diseases.

Respondent 12

European biotechnology medium company CSO is the co-founder of a biopharmaceutical company, and manages its scientific programs, collaborations and participations in European consortia.

Respondent 13

European biotechnology medium company Chief Scientific Officer in a company developing vaccines having started his career in a prestigious academic laboratory.

Respondent 14

European biotechnology medium company COO of a biopharmaceutical company dedicated to product discovery and development. He started as a scientific researcher, and has since been involved in the management of many companies.

  wave of biotech firms made their way to the market via strong partnerships with pharmaceutical companies, and today are the two are often integrated vertically, with most biotech firms specialising in one or two elements of the classic drug development value chain (fig 1).

Figure 1 :

 1 Figure 1: The traditional drug development value chain

  is a company of fifty employees, founded in 2000 which specializes in engineering and providing high value added services. OpteX has developed two competencies: process optimisation and knowledge architecture. At the beginning the company proposed one business model based on process optimisation: the technology platform, and developed know-how about optimisation of research and drug production processes. In 2004 it offered a complementary service -based on the same core competence but with a different value proposition -by becoming a contract manufacturer, producing specific molecules for pharmaceutical companies.While the therapeutic domains addressed may be different, the technology underlying its two main business models -technology platform and contract manufacturingremains the same. In 2008 OpteX started marketing technology brokering services, based on its new knowledge architecture competence that was building on its network of partners, clients and suppliers.Betwin was founded in 2007 and employs a staff of five. Its core competency is knowledge architecture: the company buys a drug candidate at early development stages (first proof of efficiency or preclinical trials), then develops it through an array of partners and suppliers and resells the drug candidate after later trials (for example after phase II). Betwin has bought three molecules in different therapeutic areas in the past two years, replicating the same business model each time.

  Technology platform activities include engineering and technological developments, molecule optimisation, screening, etc., and generally focus on the early drug development phases. They are many forms of platforms (such as open technology platforms, proprietary platforms, etc.) but broadly speaking they provide technological developments through service contracts or act as co-developers at some product development steps. This technological development activity creates value, which is then captured through the price of the service or the success of the drug or technology co-developed. The time-to-market is short to medium, and risks are low (in the simpler service case) or medium for co-development activities. Interdependency and expected returns are also low or medium, again depending on the form (contract or co-development partnership) of the activity. Contract manufacturing organisations (CMO) is a business model which is based on optimisation processes for the production of preclinical, clinical and final batches for other actors of the value chain.

Respondent 15 European

 15 biotechnology medium company CFO for a product-based firm. He has worked in the biotechnology industry for many years and conducted two Initial Public Offerings in different companies Respondent 16 CFO of European biotechnology medium company European biopharmaceutical company quoted on the NASDAQ.

Table 1 : General characteristics of biopharmaceutical business models

 1

	Core Competence	Time-to-	Risk	Interdependenc	Expected
		market		y with other	returns
				firms
	Drug Discovery	Long	High	High	Very High
	Knowledge	Medium Medium	High	High
	Architecture
	Process Optimisation Short	Low/Medium Low	Low

  trial stages are shorter. Risks are medium because the probability of failure is low, but drug development still requires investments for later trials and marketing. Interdependency with other firms is important as it is key to detecting opportunities, and successful alliances are necessary for taking the drug to its new

	market development: thus dividing the intellectual property rights can become a
	central (sometimes difficult) issue. These companies have a product that can go to the
	market quickly: investments can be recouped and cash starts flowing. Expected
	returns are high.
	Brokering activities establish linkages between technologies and firms which are not
	yet connected. In this context it refers to the detection of a molecule in one
	Repurposing activities consist on choosing a molecule in development or on the
	market for one therapeutic domain and repositioning it towards a new therapeutic
	domain. Choosing already developed molecules allows these firms to re-start the
	product development chain without having to go through the discovery phase. The
	value is created by the detection of the opportunity and the development of the

They develop virtual, repurposing or brokering activities to rearrange knowledge and benefit from it. Virtual firms focus on drug development activities, but firms have no laboratory or manufacturing capabilities and outsource almost everything, coordinating research that is actually performed by its partners. The business logic and source of value of this model are in orchestrating a network to develop a drug product, with product price and time and cost savings as the means of capturing value. Such virtual activities speed up the process of drug development and promise a medium time to market. As research execution is shared amongst the different partners, risks are medium as less investment is required than in an integrated company, although -of course -this model is still sensitive on drug failure. Interdependency with other firms is high because the core activity of virtual firms depends entirely on its partner alliances for access to knowledge, equipments and markets. The returns expected to successful drug development are high. product, and captured from time and costs savings as well as from product sales. The time-to-market is short to medium because the safety and efficacy of the repurposed molecules have already been proved in their original therapeutic domain, so pre-clinical and clinical company's pipeline that could interest another company, making the connections and managing the transaction between the two firms

[START_REF] Zhang | Brokerage and firm network evolution: The Complementary Roles of Architectural and Functional Brokers[END_REF]

. The market is not the drug market but biopharmaceutical firms. Detecting the opportunity is the source of the value creation, and value is captured when the technology broker receives a commission. So the time-to-market is very short and the risks are low (drug failure has no impact and few investments are needed). Interdependency is high, as it is the basis of the company's business, but, clearly, expected returns are low compared to those of a product company.

Table 2 :

 2 Adapted business models

	Criteria	Firms	Core	Time to	Risks	Inter-	Expected
	Business Model		competence	Market		dependency	returns
	Collaboration for	OphSmart,	Discovery	Long	Medium	High	High
	discovery	Emics
	Repurposing	OphSmart	Knowledge	Medium	Medium	High	High
			architecture
	Virtual	OphSmart,	Knowledge	Medium	Medium	High	High
		Emics,	architecture
		Betwin
	Technology	OpteX	Knowledge	Short	Low	High	Low
	Brokering		architecture
	Technology	OpteX	Process	Short/ me	Low/ Me	Medium/high Medium
	Platform		Optimisation	dium	dium
	Contract	OpteX	Process	Short	Medium	Low	Medium
	manufacturing		Optimisation
	organisation

Table 3 : Business model portfolios

 3

		OphSmart	Emics	OpteX	Betwin
	Core	1 Drug	1 Drug	1 Process	1 Knowledge
	competencies	discovery	discovery	Optimization	Architecture
		2 Knowledge	2 Knowledge	2 Knowledge
		Architecture	Architecture	Architecture
	Business	1 Collaboration	1 Collaboration	1 Technology	1 Virtual
	models	for Discovery	for Discovery,	Platform,
		2 Virtual,	2 Virtual	2 Technology
		3 Repurposing		Brokering
				3 CMO
	Relationships	Complementary	Complementary	Complementary	Replication of
	between	at different stage	at different stage		same business
	business	of the value	of the value		model
	models	chain	chain
	Therapeutic	Identical	Identical	Identical (or close) Different
	domains

Table 3 : Balancing business models portfolio

 3

	Risk and time to market

  Respondent 1 Vice-President (in charge of European activities) for one of the largest biopharmaceutical companies famous for its biotechnology products He has worked in the biopharmaceutical industry for many years, having started as a scientist thirty years ago in a big pharmaceutical company Respondent 2 Scientific Director in charge of scientific strategy of a world competitive cluster providing a healthcare shield against infectious diseases, and centre of excellence in diagnostics and vaccines. Respondent 3 Research Director in a National Institute of Health. Having focused for years on antibodies research, he runs a team looking for therapeutic targets and drug candidates. Respondent 4 Head of the pharmaceutical department, French Ministry of Finance and Economics, this respondent negotiates with biopharmaceutical companies about reimbursement levels and French market access conditions. Respondent 5 Research Director in a Centre for the Study of Drug Development, a specialist observer of the biotechnology industry over years and published widely in academic as well as managerial reviews. Respondent 6 CEO of European Start-up who has worked in the biopharmaceutical industry for more than twenty years and previously founded two other start-ups. His current start-up, founded in 2006, is a nanomedicine company that intends to revolutionize drug delivery and drug addressing. Respondent 7 CEO of European Start-up (founded in 2006) in parallel with his position of Research Director in a National Health Institute. Start-up business based on a promising technology for radio labelling, preclinical studies and radiopharmaceuticals synthesis; intending to assist other companies in strategic decisions for selecting and developing drug candidate molecules/biomolecules. Respondent 8 European Start-up Chief Scientific Officer, founder of an information technology firm that provides customized IT solutions for drug development or patients' observations. Respondent 9 European biotechnology medium company CEO who founded his first company in 2000 to provide propose high value added services for drug development.

Appendix 2: Extracts of interviews that helped us to determine the three core competences present on the drug development value chain. When the core activity of the firm depends upon alliances with other partners.

Core competence at basis of activity

Expected returns Low Medium High

Expected returns are the turnovers that will be created by the activity. The maximum is the turnover produced by a blockbuster drug. In comparison the price of a service is a low return.

Low value added services.

High value added services or pharmaceutical production.

Drugs on the market.


Thai V Hoang

Salvatore Tabbone
email: tabbone@loria.fr

Ngoc-Yen Pham
email: ngoc-yen.pham@mica.edu.vn

Extraction of Nom Text Regions from Stele Images Using Area Voronoi Diagram

Automatic processing of images of steles is a challenging problem due to the variation in their structures and body text characteristics. In this paper, area Voronoi diagram is used to represent the neighborhood of connected components in stele images containing Nom characters. Body text region is then extracted from stele images by the selection of appropriate adjacent Voronoi regions based on the information about the thickness of neighboring connected components. Experimental results show that the proposed method is highly accurate and robust to various types of stele.

Introduction

The cultural inheritance of a country is a public property which represents its collective and alive memory and forms a solid base for its future development. Its digitalization is essential for the maintenance and promotion of cultural diversity in this era of globalization. Today, the exploitation of collections of images of old steles has not yet found a satisfactory answer due to their unstructured nature. This raises a problem in the generation of a database, presented in the form of heterogeneous documents, allowing search for information and navigation within these corpora.

A collection of approximately 30.000 stele images containing Nom characters, a derivative of Chinese which was used in Vietnam before the 20th century, has been prepared by EFEO (The French School of Asian Studies) along with their Vietnamese annotation. There is no need to recognize and translate these Nom characters into Vietnamese because their exact interpretation is still under research today. It is needed, however, to process these stele images and represent characters in a database then Nom linguists can do character retrieval in their work. As the initial step of this process, body text regions need to be extracted from stele images.

Locating text regions in a text-containing image is fun-

Figure 1. A stele image

damental for its automatic processing. Text-containing regions in stele images are usually encompassed by loosely structured decoration. Figure 1 shows an example of a stele image in which the body text region lies at the center of the image and is enclosed by decorative patterns. This decoration is not only complex in each stele but also varies among different steles. This makes extraction of body text regions extremely difficult. Some methods have been proposed during the past decades to extract regions of document components such as text, figures, tables, and halftones. Fletcher and Kasturi [START_REF] Fletcher | A robust algorithm for text string separation from mixed text/graphics images[END_REF] employed Hough transform to the centroids of the rectangles enclosing connected components in order to group together connected components into logical character string. This method is not suitable for Nom characters composed of unconnected strokes. In [START_REF] Jain | Text segmentation using Gabor filters for automatic document processing[END_REF], Jain and Bhattacharjee proposed a texture method using Gabor filter to segment text from document images. This method is time-consuming and inapplicable to stele images as the decorative texture sometimes resembles the texture of body text region.

Recently, some researchers used Voronoi diagram and Delaunay triangulation for document image analysis. For example, Kise et al. [START_REF] Kise | Segmentation of page images using the area Voronoi diagram[END_REF] and Lu et al. [START_REF] Lu | Word grouping in document images based on Voronoi tessellation[END_REF] used Voronoi diagram for page segmentation and word grouping in document images respectively using the distance and area ratio between neighboring connected components. Xiao and Yan [START_REF] Xiao | Text region extraction in a document image based on the Delaunay tessellation[END_REF] employed Delaunay triangulation to extract text regions by representing each connected component by its centroid. However, all of these methods work only with Latin characters and are inappropriate to extract Nom text region from stele images. This is because one Nom character may be composed of several connected components whose centroids, areas and inter-distances do not have any regularity.

In this paper, we propose an efficient method based on connected component analysis for the extraction of body text region from stele images. Area Voronoi diagram is employed to represent the neighborhood of connected components. Based on this representation, the process of body text region extraction can be considered as the grouping of appropriated adjacent Voronoi regions. For this purpose, we utilize one characteristic feature of the standard deviation of the thickness of neighboring connected components.

The remainder of this paper is organized as follows. Section 2 briefly gives a basic definition followed by a constructing method of area Voronoi diagram. Section 3 describes the details of body text region extraction using standard deviation of the thickness of neighboring components. Experimental results are given in Section 4, and finally conclusions are drawn in Section 5.

Area Voronoi diagram

Area Voronoi diagram definition

Let G = {g 1 , . . . , g n } be a set of nonoverlapping components in the two dimensional plane, and let d(p, g i ) be the Euclidean distance between a point p and a component g i defined by:

d(p, g i ) = min q∈gi d(p, q) (1)
Then the Voronoi region V (g i ) and the area Voronoi diagram V (G) are defined by:

V (g i ) = {p|d(p, g i ) ≤ d(p, g j ), ∀j = i} (2) V (G) = {V (g 1 ), . . . , V (g n )} (3)
The boundaries of Voronoi regions, which are always curves, are called Voronoi edges, and the points where Voronoi edges meet are called Voronoi points.

The Voronoi region of each image component corresponds to a portion of the two dimensional plane. It consists of the points from which the distance to the corresponding component is less than or equal to the distance to any other image components.

Area Voronoi diagram construction

There are two main approaches that are usually used for the construction of area Voronoi diagram:

-The first approach constructs area Voronoi diagram by means of first generating a point Voronoi diagram for a set of sampled points lying on the boundaries of image components and then deleting Voronoi edges generated from points of the same components [START_REF] Burge | Using the Voronoi tessellation for grouping words and multi-part symbols in documents[END_REF][START_REF] Kise | Segmentation of page images using the area Voronoi diagram[END_REF].

-The second approach constructs area Voronoi diagram by labeling the image components then applying morphological operations to expand their boundaries until two expanding labels meet [START_REF] Lu | Constructing area Voronoi diagram based on direct calculation of the Freeman code of expanded contours[END_REF][START_REF] Okabe | Spatial tessellations: Concepts and applications of Voronoi diagrams[END_REF].

The first approach is time consuming as it depends on the number of sampled boundary pixels in which a high sampling rate is preferred to obtain an accurate area Voronoi diagram. We utilize here the second approach to construct area Voronoi diagram of stele images using the Euclidean distance metric [START_REF] Okabe | Spatial tessellations: Concepts and applications of Voronoi diagrams[END_REF].

Extraction of body text region

As shown in Figure 2(d), each connected component in stele image is represented by one Voronoi region. The process of body text region extraction is then considered as the process of grouping adjacent Voronoi regions which represent the body text region. Therefore, we need criteria for grouping appropriate adjacent Voronoi regions from the area Voronoi diagram.

By observation of Figure 1 we realize that body text region contains connected components of Nom characters that have similar stroke thickness. These components are, in addition, represented by adjacent Voronoi regions. On the contrary, decorative region contains heterogeneous connected components. From this viewpoint, we employ standard deviation of the thickness of neighboring connected components as the discriminating criterion between body text and decorative regions.

Standard deviation of the thickness of neighboring connected components

In order to calculate the standard deviation of the thickness of neighboring connected components, the thickness We propose here a simple but efficient enough method to calculate the thickness of a connected component using Euclidean distance map. The thickness of a connected component g i is approximately defined as two times the average of shortest distances from the pixels lying on its skeleton to its boundary.

In determining the skeletons from the Euclidean distance map obtained in Section 2.2, the algorithm in [START_REF] Di Baja | Well-shaped, stable, and reversible skeletons from the (3,4)-distance transform[END_REF] has been utilized. Figure 3 gives an example of calculating the thickness of a connected component. Original character, its distance map, and its skeleton are given in Figure 3 The standard deviation of the thickness of two neighboring connected components g i and g j , by definition, is calculated by: std(g i , g j ) = thickness(g i )thickness(g j ) 2 (4)

Grouping adjacent Voronoi regions

According to (4), the value of std among body text region is small while its value among decorative region is highly diverse. Figure 4 gives the histogram of std extracted from the stele image in Figure 1. There are two distinct clusters in the histogram:

-The cluster of higher value of std corresponds to neighboring connected components with high variance in their thickness and therefore these components belong to the decorative region.

-The cluster of lower value of std corresponds to neighboring connected components with small variance in their thickness, however these components may belong to the body text region or the decorative region. This is because the decorative region may contain neighboring connected components of similar thickness.

T std = max i,j std(g i , g j ) 2 (5)
and the adjacent Voronoi regions containing body text components thus should satisfy:

std(g i , g j ) < T std (6)
The value of T std is dependent on each image. For the stele image in Figure 1, T std has the value 5.76.

As said before, the standard deviation is sometimes not enough to discriminate all the connected components lying at the decorative region because in that region neighboring connected components may have std less than T std (an example is in Figure 5(b) in which the gray level of each Voronoi region is proportional to the thickness of the connected component belonging to that region). We utilize one more criterion based on the observation that a Voronoi region, which contains a body text component, should not has many adjacent regions as follows:

N (g i ) < N max (7)
where N (g i ) is the number of adjacent regions of a Voronoi region containing component g i and N max is a fixed value of 20 determined by experiment. -Step 1: Select the Voronoi region lying at the center of the image, push and insert its label g i to a queue Q and a list L respectively.

-Step 2: Pop a label g i from Q and select all its adjacent Voronoi regions that have not been selected before. Among these selected Voronoi regions, only keep regions with labels g j s that satisfy ( 6) and [START_REF] Lu | Constructing area Voronoi diagram based on direct calculation of the Freeman code of expanded contours[END_REF]. Push and insert g j s to Q and L respectively.

-Step 3: Iterate Step 2 until Q gets empty. At this point, L contains the labels of adjacent Voronoi regions containing body text components.

Experimental results

The proposed method has been evaluated on a database of 205 stele images of different types of character fonts, sizes and stele decorations provided by EFEO. Figure 6 shows two examples of body text extraction results.

The two input binary stele images are shown in Figure 6(a) and 6(e). The constructed area Voronoi diagrams are shown in Figure 6(b) and 6(f) in which the gray value of each Voronoi region is proportional to the thickness of the connected component belonging to that region. As shown in the figure, the body text regions and decorative regions in Figure 6(b) and 6(f) are composed of Voronoi regions of similar and diverse gray value respectively. Adjacent Voronoi regions spreading from the Voronoi region lying at the center of the image are grouped together using the process described in Section 3.2. Figure 6(c) and 6(g) show the grouped Voronoi regions. The connected components belonging to those grouped regions, which correspond exactly to the body text, are shown in Figure 6(d) and 6(h).

The ground-truth data for each stele image has been defined by hand in which body text region is defined as a set of all Voronoi regions each containing a body text component. The accuracy is defined as the proportion of body text region pixels extracted. The performance of proposed method has an accuracy of 85.81% and is tabulated in Table

Conclusions

This paper has presented a method for the extraction of body text regions from stele images using area Voronoi diagram and the homogeneity of connected components. Area Voronoi diagram has demonstrated to be effective in representing the neighborhood of connected components in a digital image. Adjacent Voronoi regions are grouped together based on the information about the thickness of neighboring connected components. The experimental results on a number of stele images have shown that the proposed method has achieved high accurate results and is robust to various types of stele.

Future work is to extract each Nom character from the obtained body text regions and represent them in a database for later retrieval. High-level semantic knowledge of these characters will then be utilized to improve the performance of body text region extraction from stele images.

Figure 2

 2 demonstrates the steps of constructing area Voronoi diagram for a stele image containing Nom characters. Original binary image I is given in Figure 2(a).

Figure 2 (

 2 b) demonstrates the Euclidean distance map. The area Voronoi diagram V is shown in Figure 2(c). Voronoi regions with their corresponding connected components are given in Figure 2(d).

Figure 2 .

 2 Figure 2. Construction of area Voronoi diagram

Figure 3 .

 3 Figure 3. Distance map and skeleton of a single connected component

Figure 4 .

 4 Figure 4. Histogram of the std By separating the two clusters in this histogram we can discriminate connected components which have high thickness variation with one of their neighbors from the remaining ones. The threshold value T std that separates the two clusters is simply determined by:

Figure 5 .

 5 Figure 5. Voronoi regions of a stele image

Figure 6 .

 6 Figure 6. Experimental results of body text extraction from stele images

Table 1 . Performance of body text region ex- traction from stele images

 1 1. There are two sources of error: one is over-grouping that is Voronoi regions from decorative region are considered as containing body text components; the other is splitting in which Voronoi regions in body text region are considered as belonging to decorative region.

		EFEO database
	Accuracy(%)	85.79
	Over-grouping(%)	11.70
	Splitting(%)	2.51

Acknowledgements

This work is supported by CNRS and EFEO under the framework of an International Scientific Cooperation Program (PICS: 2007-2009).


Thai V Hoang

Salvatore Tabbone
email: tabbone@loria.fr

Ngoc-Yen Pham
email: ngoc-yen.pham@mica.edu.vn

Recognition-based Segmentation of Nom Characters from Body Text Regions of Stele Images Using Area Voronoi Diagram

Keywords: Recognition-based segmentation, area Voronoi diagram, stele images, Nom characters, horizontal overlap, segmentation graph

Segmentation of Nom characters from body text regions of stele images is a challenging problem due to the confusing spatial distribution of the connected components composing these characters. In this paper, for each vertical text line, area Voronoi diagram is employed to represent the neighborhood of the connected components and Voronoi edges are used as nonlinear segmentation hypotheses. Characters are then segmented by selecting appropriate adjacent Voronoi regions. For this purpose, we utilize the information about the horizontal overlap of connected components and the recognition distances of candidate characters provided by an OCR engine. Experimental results show that the proposed method is highly accurate and robust to various types of stele.

Introduction

Stone steles in Vietnam usually contain Nom characters, a derivative of Chinese which was used before the 20th century describing important historical events. Today, the exploitation of these steles is necessary to better understand the history and form a solid base for future development. Automatic processing of stele images composes of three sub-problems: extraction of body text regions, segmentation of Nom characters, and finally representation of these Nom characters in a database allowing search for information. The first one has been tackled using the information about the thickness of connected components [START_REF] Hoang | Extraction of Nom text regions from stele images using area Voronoi diagram[END_REF]. Fig. 1(a)-1(b) show an example of a stele image and its extracted body text region respectively.

As shown in Fig. 1(b), Nom characters are engraved on stone steles in vertical text line from right to left and each character may be composed of several connected components. In each text line, the gaps between characters is indistinguishable from the gaps between connected components belonging to one character. This makes segmentation of Nom characters a challenging problem to be dealt with in this paper.

There exists many methods in literature for character segmentation. Lu [START_REF] Lu | Machine printed character segmentation -An overview[END_REF], Lu and Shridhar [START_REF] Lu | Character segmentation in handwritten words -An overview[END_REF] reviewed methods for character segmentation in machine printed documents and handwritten works. Casey and Lecolinet [START_REF] Casey | A survey of methods and strategies in character segmentation[END_REF] classified existing methods into three strategies: dissection, recognition-based, and holistic. The first strategy decomposes the image into a sequence of sub-images using general features like character height, width and white space between characters. In the second strategy, the system searches the image for components that match classes in its alphabet. The third strategy seeks to recognize the words as a whole avoiding the need to segment into characters. This strategy is inappropriate for Nom characters as each Nom character has its own meaning.

Tseng and Chen [START_REF] Tseng | Segmenting handwritten Chinese characters based on heuristic merging of stroke bounding boxes and dynamic programming[END_REF] proposed a method for Chinese character segmentation by first generating bounding boxes for character strokes then using knowledgebased merging operations to merge these bounding boxes into candidate boxes and finally applying dynamic programming algorithm to determine optimal segmentation paths. However, the assumption of similarity on character sizes makes this method unsuitable for Nom characters written vertically. Viterbi algorithm and background thinning method were used in [START_REF] Tseng | Recognition-based handwritten Chinese character segmentation using a probabilistic Viterbi algorithm[END_REF][START_REF] Zhao | Two-stage segmentation of unconstrained handwritten Chinese characters[END_REF] to locate nonlinear segmentation hypotheses separating handwritten Chinese characters. These methods are also inappropriate for Nom characters as there may exist horizontal gaps inside a character separating its connected components and connected components from neighboring characters on the same line may horizontally overlap.

Area Voronoi diagram has been used by some researchers for document image analysis. For example, Kise et al. [START_REF] Kise | Segmentation of page images using the area Voronoi diagram[END_REF] and Lu et al. [START_REF] Lu | Word grouping in document images based on Voronoi tessellation[END_REF] used area Voronoi diagram for page segmentation and word grouping in document images respectively using the distance and area ratio between neighboring connected components.

However, these methods work only for alphanumeric documents in which each character is represented as a connected components.

In this paper, we propose an efficient method combining dissection and recognition-based strategies. For each vertical text line extracted from the body text region using vertical projection profile, area Voronoi diagram is employed to represent the neighborhood of connected components and Voronoi edges are used as nonlinear segmentation hypotheses. Adjacent Voronoi regions are first grouped using the information about the horizontal overlap of connected components. The remaining Voronoi edges are used as vertices in a segmentation graph in which the arcs' weights are the recognition distances of the corresponding candidate characters using an OCR engine. The vertices in the shortest path detected from the segmentation graph represent the optimal segmentation paths.

The remainder of this paper is organized as follows. Section 2 briefly gives a basic definition of area Voronoi diagram. Section 3 presents a method to group adjacent Voronoi regions using horizontal overlap of connected components. Section 4 describes the details of the algorithm determining optimal segmentation paths using recognition distances of candidate characters. Experimental results are given in Section 5, and finally conclusions are drawn in Section 6.

Area Voronoi Diagram

Let G = {g 1 , . . . , g n } be a set of non-overlapping connected components in the two dimensional plane, and let d(p, g i ) be the Euclidean distance between a point p and a connected component g i defined by d(p, g i ) = min q∈gi d(p, q), then Voronoi region V (g i ) and area Voronoi diagram V (G) are defined by:

V (g i ) = {p | d(p, g i ) ≤ d(p, g j ), ∀j = i} V (G) = {V (g 1 ), . . . , V (g n )}
The Voronoi region of each image component corresponds to a portion of the two dimensional plane. It consists of the points from which the distance to the corresponding component is less than or equal to the distance to any other image components. The boundaries of Voronoi regions, which are always curves, are called Voronoi edges.

To construct area Voronoi diagram, we utilize the approach represented in [START_REF] Lu | Constructing area Voronoi diagram based on direct calculation of the Freeman code of expanded contours[END_REF] that first labels the image components and then applies morphological operations to expand their boundaries until two expanding labels are met. Fig. 2(a)-2(d) show the area Voronoi diagram for a column text line extracted from the body text region in Fig. 1(b). Original text line I is given in Fig. 2(a). Fig. 2(b) demonstrates the Euclidean distance map in which the gray value of each pixel is proportional to the distance from that pixel to the nearest connected component. The area Voronoi diagram V of I is shown in Fig. 2(c). Voronoi regions with their corresponding connected components are given in Fig. 2(d).

As shown in Fig. 2(d), each connected component is represented by one Voronoi region and Voronoi edges can be used as nonlinear segmentation hypotheses. The process of character segmentation is then considered as the process -Step 1 : Voronoi regions are first grouped using the criteria based on the degree of horizontal overlap of their corresponding connected components. We argue that connected components from one vertical text line overlapped horizontally to a certain degree should belong to one character. This algorithm is described in Section 3. -Step 2 : As each Nom character may be composed of several connected components and there may exist horizontal gaps between these connected components, the above algorithm does not guarantee a grouped Voronoi region for each character. We further group Voronoi regions using the recognition distances of candidate characters in Section 4.

(a) (b) (c) (d) (e) (f) (g)

Grouping of Voronoi Regions Using Horizontal Overlapping Profile of Connected Components

In order to calculate the degree of horizontal overlap of two connected components, we define T i and B i as the top and bottom coordinates of the bounding box of the connected component g i (B i > T i ). The degree of horizontal overlap V O ij of two connected components g i and g j is calculated as:

V O ij = max{min(B i -T j , B j -T i ), 0} min(B i -T i , B j -T j ) (1)
The numerator of ( 1) is interpreted as the length of the overlapping segment and V O ij is the proportion of the shorter connected component being overlapped. Thus two connected components g i and g j which have V O ij ≥ V O thr are considered as belonging to one character and their corresponding Voronoi regions are grouped. Fig. 2(e) provides the horizontal projections of the bounding boxes of connected components in Fig. 2(a) with each vertical line corresponds to one connected component. The grouped Voronoi regions are shown in Fig. 2(f) and adjacent lines correspond to each grouped Voronoi region are labeled using the same color in Fig. 2(e).

By observation of Fig. 2(d) we realize that if each Nom character is represented by one group of Voronoi regions, these grouped region should span from the left border of the text line to its right border. From this viewpoint, those Voronoi regions that do not span from left to right in Fig. 2(f) need to be further grouped to one of its adjacent regions. We propose to use the distance d ij between neighboring connected components g i and g j as the criterion of grouping:

d ij = min pi∈gi,pj ∈gj d(p i , p j )
where d(p i , p j ) is the Euclidean distance between p i and p j . Thus, for each Voronoi region i to be further grouped, we select a region j from a set of its adjacent regions D by j = argmin k∈D d ik and then group region i with region j. The resulting grouped Voronoi regions are provided in Fig. 2(g).

Recognition-based Determination of Optimal Segmentation Paths

The validity of segmentation hypotheses in Fig. 2(g) is verified by feeding candidate characters into an OCR engine and using their recognition distances to determine the optimal segmentation paths. A segmentation graph is constructed by using the segmentation hypotheses as its vertices and recognition distance of the candidate character corresponding to vertex i and vertex j as the weight of the arc connecting i and j. The segmentation graph in Fig. 2(h) has 31 vertices corresponding to 29 segmentation hypotheses in Fig. 2(g) plus the top and bottom lines. Assuming that a lower value of recognition distance corresponds to higher confidence of the OCR engine in the candidate character, the optimal segmentation paths are thus determined by finding the shortest path in the segmentation graph. For the graph in Fig. 2(h), we need to find the shortest path from vertex 1 to 31.

In updating the weights of the graph, instead of feeding all candidate characters into the OCR engine, we only feed candidate characters that have the character-like feature H ≤ H thr W where H and W are the height and width of the candidate character. By doing this, candidate character covering at least two Nom characters are mostly discarded. The arcs shown in Fig. 2(h) correspond to all candidate characters that have the character-like feature.

As there exists no OCR engine for Nom characters, we employ a Chinese OCR engine [11] admitting that not all Nom characters are recognizable by the engine. However, as the proportion of Nom characters that are not Chinese characters are small, the shortest path algorithm can overcome the case in which one non-Chinese character lies between two Chinese characters. An example of a line segment of Nom characters along with its segmentation graph are given in Fig. 3(b) and 3(a) respectively. The optimal segmentation paths in this case contains vertices {22, 24, 26, 28}. For the full segmentation graph in Fig. 2(h), directly applying a shortest path algorithm to the may result in error due to the inappropriateness of the OCR engine, we propose here a three-steps algorithm to find the shortest path of the segmentation graph:

-Find cut vertices (sky blue vertices in Fig. 2(h)): a cut vertex is a vertex the removal of which disconnect the remaining graph, the shortest path of the graph must contain cut vertices.

-Find arcs having corresponding candidate characters of high confidence (read arcs in Fig. 2(h)): candidate characters that have recognition distances less than RD thr are said to be of high confidence and the arcs corresponding to these characters should be included in the shortest path.

-Find the shortest paths of the remaining subgraphs. The final optimal segmentation paths are shown in Fig. 2(i).

Experimental Results

In order to determine the appropriate values of V O thr and H thr , a learning set of 16 Nom text lines containing 280 characters have been used for the evaluation of the proposed algorithm's accuracy at different threshold values. According to Fig. 4, the values of V O thr and H thr are selected as 0.4 and 1.25 respectively corresponding to the maxima of the curves. The value of RD thr , which depends on the OCR engine, is selected experimentally as 0.06. The remaining images are regarded as insufficient because of their too noisy body text regions or their poor resolution. We have randomly selected 20 stele images containing 4998 Nom characters for experiment. The ground truth for these characters are defined by hand. Table 1 summarizes the experimental results. The accuracy which is defined as the percentage of characters that are segmented correctly has the value 89.14%. There are two sources of error: one is missing and the other is incorrect grouping. The error of missing concerns with characters that are classified as background noise. In incorrect grouping, each segmented character is not composed of all the connected components from one Nom character, its connected components may come from background noise or neighboring Nom characters. A considerable amount of error comes from the steles which contain characters of various sizes and curved text lines in their body text regions. The layout of these steles thus cannot be aligned into vertical straight lines. This results in error in the extraction of text lines from body text regions using vertical projection profile and consequently the segmented characters will be inaccurate.

Conclusions

In this paper, area Voronoi diagram has demonstrated to be effective in representing the neighborhood of connected components in digital images. Voronoi edges then function as nonlinear segmentation hypotheses that need to be validated. Adjacent Voronoi regions have been first grouped using the information about the vertical overlap of connected components. The remaining Voronoi edges are used as vertices in a segmentation graph in which the weight of each arc is the recognition distance of the corresponding candidate character provided by an OCR engine. The vertices in the shortest path of the segmentation graph represent the optimal segmentation paths. Experimental results on a number of stele images show that the proposed method is highly accurate. Further work will employ a curved text line extraction algorithm [START_REF] Likforman-Sulem | Text line segmentation of historical documents: a survey[END_REF] to increase the accuracy and represent each Nom character in a database for later retrieval. Moreover, poor-resolution images will be re-scanned and noise in body text regions will be removed to make this method more applicable.

Fig. 1 .

 1 Fig. 1. A stele image (a) and its extracted body text region (b)

Fig. 2 .

 2 Fig. 2. Steps in segmenting Nom characters from one vertical text line

Fig. 3 .

 3 Fig. 3. (a) A line segment of Fig. 2(g), (b) Its corresponding segmentation graph

Fig. 4 .

 4 Fig. 4. Algorithm's accuracy at different values of V O thr and H thr

Table 1 .

 1 Performance of Nom character segmentation

		EFEO database
	Accuracy(%)	89.14
	Missing(%)	0.84
	Incorrect grouping(%)	10.02

⋆ This work is supported by CNRS and EFEO under the framework of an International Scientific Cooperation Program (PICS: 2007-2009).