PhD Etienne Yoan Miot
email: miot.yoan@gmail.com

Growth and Shrinkage in Three French Traditional Industrial cities: Mulhouse, Roubaix and Saint

Keywords: shrinkage courses, traditional industrial cities, suburbanization, professional migration

(Authier and al., ) between 2003(Authier and al., and 2008. . While the first analysis substantiates the conventional results of shrinkage, the second provides new elements concerning the role of employment and economic development in the demographic shrinkage process.

INTRODUCTION

The urban shrinkage topic is not new (Cunningham-Sabot and Fol, 2010) but it is increasingly useful for characterizing the weakening process of traditional industrial urban areas in MEDCs. Between 1960 and[START_REF] Couch | Decline and sprawl : an evolving type of urban development observed in Liverpool and Leipzig[END_REF], one half of the 364 biggest city-regions in Europe have experienced an urban shrinkage process, 116, a persistent process and 13, a permanent process. In this category, every city-region is a traditional industrial urban area, such as the Ruhr region and the Midlands.

In this structural crisis context for these cities since the late 1960's, researchers in urban and regional science have developed, at the turn of the century, the concept of urban shrinkage, which supersedes the concept of urban decline. If urban decline is cyclical (Van der Berg, 1983), urban shrinkage could be defined as a durable breakup in the spatial, demographic, economic and social organization of territories in which weakening processes prevail [START_REF] Baron | Villes et régions européennes en décroissance -maintenir la cohésion territoriale[END_REF]. There are many major causes: deindustrialization and suburbanization in the United States and in Western Europe [START_REF] Gillette | Camden After the Fall : Decline and Renewal in a Post-Industrial City[END_REF][START_REF] Beauregard | Urban Population Loss in Historical Perspective : United States, 1820-2000[END_REF][START_REF] Albecker | The Effects of Globalization in the First Suburbs of Paris: From Decline to Revival ?[END_REF], post-socialist transformations in Eastern Europe [START_REF] Bontje | Facing the challenge of shrinking cities in East Germany : the case of Leipzig[END_REF] and the second demographic transition in Central and Southern Europe and Japan (Van de [START_REF] Van De Kaa | Europe's Second Demographic Transition[END_REF]Buhnik, 2010). These transformations create a vicious circle in which multiple factors and various interrelated symptoms are combined: unemployment and social difficulties are on the rise, leading to new social expenditure although tax revenues are decreasing and spending must be required to attract new activities. Industrial brownfields are spreading due to an economic downturn while demographic losses increase housing and commercial vacancies. Finally, the city's image deteriorates, reinforcing its demographic losses and lack of investment in new economic activities.

Moreover, this downward spiral is spatially unevenly distributed. Many studies have shown that urban shrinkage increases the socio-spatial divisions in urban areas, reinforced by the policies implemented [START_REF] Fol | Urban shrinkage and socio-spatial disparities: are remedies worse than the disease ?[END_REF]. Growing neighbourhoods are quite close to declining ones. When a regeneration project is developed in city-centres, population and employment grow, and housing and commercial vacancies decrease, but inner city rings shrink more and more [START_REF] Couch | Decline and sprawl : an evolving type of urban development observed in Liverpool and Leipzig[END_REF][START_REF] Beauregard | Urban Population Loss in Historical Perspective : United States, 1820-2000[END_REF]. Conversely, when inner cities are shrinking, the suburban rings are continually growing, concentrating new economic activities and new housing developments.

In this context, this paper aims to explain why socio-spatial division process is one of the major characteristics of shrinking urban areas and why growing and shrinking neighbourhoods are in close reach. To address this question, two methods are developed. The first is a long-term approach based on a multidimensional statistical analysis [START_REF] Beauregard | Urban Population Loss in Historical Perspective : United States, 1820-2000[END_REF] and the second is a dynamic analysis based on statistical analysis of residential careers [START_REF] Authier | Etat des lieux sur les trajectoires résidentielles, PUCA (plan urbanisme construction architecture)[END_REF]. Three steps will be followed. First, we will present our conceptual framework to analyse the shrinkage process in Mulhouse, Roubaix and Saint-Etienne. Then, through our three case studies, the results of each methodology will be examined.

SHRINKAGE IN TRADITIONAL INDUSTRIAL URBAN AREAS: A SOCIO-SPATIAL DIVISION DUE TO DEINDUSTRIALIZATION AND SUBURBANIZATION

1.1.

Conceptual framework: urban shrinkage of traditional industrial urban areas as a socio-spatial division due to deindustrialization and suburbanization

Decreasing population, economic downturn and socio-spatial polarization are the main characteristics of the shrinking traditional industrial urban areas.

First of all, deindustrialization, because of globalization, creates an unequal economic downturn. The economic reconversion policies generate negative results: in spite of recovery, BtoB jobs and productive sectors are growing slowly. This growth is mainly driven by public services (education, social and health services). Despite deindustrialization, industry remains the central sector of the local productive economy. It illustrates the failure of economic diversification and reconversion policies [START_REF] Tomaney | Les transformations de la base économique des villes britanniques de tradition industrielle : le cas du Nord-Est de l'Angleterre[END_REF]. In terms of spatial organization, economic transformations intensify the spatial contrasts within shrinking urban areas [START_REF] Parkinson | Requalification urbaine et développement inégal dans une ville traditionnelle : le cas de Liverpool[END_REF][START_REF] Turok | The Jobs Gap in Britain's Cities: Employment Loss and Labour Market Consequence[END_REF]: in the face of deindustrialization and economic downturn, the suburbanization in activities is making headway (Di Méo, 1989) and creates a spatially selective decline in inner cities.

Second, economic shrinkage leads to massive unemployment and increasing social difficulties. Job insecurity becomes the main experience for the working class [START_REF] Castel | Les Métamorphoses de la question sociale : une chronique du salariat[END_REF].

In traditional industrial cities, the sharp manufacturing decline demolishes all social rules. While, before the shrinking process, as in the "banlieues rouges" (Bacqué and Sintomer, 2001), social, economic and territorial communities were coexistent, unemployment and job insecurity destroyed the working class identity, creating more social heterogeneity and an outburst of social problems.

Third, while unemployment and job insecurity climb, traditional industrial urban areas become less and less residentially attractive at regional and national scales: a socially selective outmigration process is emerging. The regional and national residential careers are closely interconnected with working conditions and job opportunities. Upper classes are the main actors in this process (Baccaïni, 2007;[START_REF] Authier | Etat des lieux sur les trajectoires résidentielles, PUCA (plan urbanisme construction architecture)[END_REF]. They cause some demographic imbalances between the traditional industrial urban areas and more economically vigorous regions (Cusin, soon in print). These residential careers aggravate the social crisis effects by the social selection of the youngest, most qualified and wealthier households whilst the most deprived people remain in the city (OECD, 1983;Friedrichs, 1993).

Fourth, despite economic, social and demographic difficulties relative to deindustrialization and to selective outmigration process at a regional or a national level, suburbanization continues. This suburbanization could be considered as one of the main causes of the shrinking process in traditional industrial urban areas [START_REF] Bontje | Facing the challenge of shrinking cities in East Germany : the case of Leipzig[END_REF][START_REF] Beauregard | Voices of decline: the Postwar fate of U.S. Cities[END_REF]. However, suburbanization is problematical and paradoxical because of inner city shrinkage.

It reflects the lack of residential attractiveness in inner cities because, if suburbanization is a zero-sum game at the urban area scale, it still impacts inner cities [START_REF] Couch | Decline and sprawl : an evolving type of urban development observed in Liverpool and Leipzig[END_REF]. Therefore, urban shrinkage leads to a restructured spatial organization mix, within a short distance: shrinking neighbourhoods like old inner cities and growing ones like suburban rings [START_REF] Schmidt | Sprawl without growth in Eastern Germany[END_REF].

Finally, if suburbanization causes an uneven shrinkage within the urban area, it is also a socially selective process for inner cities. Indeed, the suburbanization has its roots in homeownership development [START_REF] Berger | Les périurbains de Paris. De la ville dense à la métropole éclatée ?[END_REF], which has a significant social impact. In fact, homeownership development is interconnected with a life cycle, when two young people are moving-in together or when they become a family [START_REF] Bonvalet | Accession à la propriété et trajectoires résidentielles[END_REF]. Moreover, homeowners belong to middle or upper classes and their professional careers are settled [START_REF] Driant | A quoi sert l'accession à la propriété ?[END_REF]. Consequently, suburbanization strengthens socio-spatial divisions and explains its power in urban areas [START_REF] Charlot | La périurbanisation renforce-t-elle la ségrégation résidentielle urbaine en France ?[END_REF] by the selection of the wealthiest, youngest and bestintegrated households of the inner cities.

In other words, considering these five interrelated factors, urban shrinkage is characterized by an increase of socio-spatial divisions at every scale and it could be considered as a durable rift in the spatial organization of the urban areas, economically, socially and demographically speaking. We will now present our methodology to study these five interrelated factors and understand their contribution to explain the shrinkage process and its unequal organization.

Methodological framework: between long-term and dynamic analyses.

In order to analyse these territorial dynamics, our methodological framework followed three steps. A description of the uneven nature of urban shrinkage is made (1.2.1.), then the territorial and temporal shrinkage courses are studied since 1968 (1.2.2.) and finally, the residential mobility is analysed to determine the main causes of urban shrinkage (1.2.3.).

Outlining the profile of the uneven nature of urban shrinkage

First, a socio-spatial segregation index in the three urban areas to analyse the uneven nature of urban shrinkage was calculated. Seven social characteristics were analysed through the segregation index of Duncan at IRIS scale1 basing our work on Preteceille 's analysis (2003). This method adds three more categories, beyond professions and socioprofessional categories: immigrants, unemployed and insecure jobholders, in order to take into consideration the increasing disintegration of the wage society [START_REF] Castel | Les Métamorphoses de la question sociale : une chronique du salariat[END_REF]. This segregation index is compared to the biggest urban areas in France, i.e. more than 200,000 inhabitants.

However, Preteceille's method doesn't take into account the role of housing stocks, although this factor plays a major role in the socio-spatial organization in an urban area. Indeed, the housing stock organizes socio-spatial divisions: social housing, owner-occupation and renting determine the social occupancy of areas. For example, at a local or national level, deprived people and working class cluster mostly where social housing is predominant [START_REF] Driant | Le parc social : premier statut d'occupation des ménages à bas revenus[END_REF]. In contrast, the owner-occupants mostly belong to upper classes or to middle-class, and are composed of families, which is due to access to bank loans and life cycles [START_REF] Driant | A quoi sert l'accession à la propriété ?[END_REF]. They mostly live in recently built single homes. In this context, the weight of owner-occupancy or social housing could influence the social profile of an area, especially as the different housing stocks are unequally distributed in the urban area. For instance, the owner-occupancy is over-represented in suburban rings, in which single homes are dominant. Social housing is concentrated in the outer city fringe because of the urbanization cycle in the 1950[START_REF] Mumford | Le déclin des villes ou la recherche d'un nouvel urbanisme[END_REF]period (Vieillard-Baron, 2011).

In this context, in order to define some housing areas within the urban area, our analysis is based on J-P Lévy's studies (1998; 2003). Indeed, J-P Lévy considers housing as a sociospatial organization interrelated with the residential career. Thus, a hierarchical clustering of the three urban areas was established on eighteen social and urban parameters (fig. 1). It aims at defining the socio-residential occupation of neighbourhoods and municipalities in order to determine both the dissimilarity of spatial units and aggregate these spatial units into homogeneous classes [START_REF] Pumain | L'Analyse spatiale[END_REF]. Thus, a typology of neighbourhoods and municipalities could be formed.

Analysing the spatio-temporal shrinkage system

Then, to illustrate the lasting breakdown in the socio-spatial organization, the courses of deindustrialization and suburbanization were studied [START_REF] Beauregard | Urban Population Loss in Historical Perspective : United States, 1820-2000[END_REF]. Our method was to reconstruct the economic, demographic and social trajectories of the Lille, Mulhouse and Saint-Etienne urban areas. It is based on the census data from 1968 to 2008, dealing with labour force, employment at the workplace and housing on the municipal scale. The change in the three urban areas was compared with the 42 biggest urban areas in France, i.e. more than 200,000 inhabitants, and the internal alterations in the three urban areas. A specialization index was developed to point out the internal spatial transformation of job localization and to indicate the economic transformation of the urban areas in the French urban network. Data demonstrate the transformation of economic zones and the labour force's structural transformation.

About the suburbanization process analysis, a study of the population trends from 1968 to 2008, based on yearly changes (migratory and natural balances), was developed. This analysis aimed at reconstructing the geography of the demographic growth and shrinkage in the three urban areas.

Studying the residential mobility to understand the main shrinkage determinants

Finally, the third step is an analysis of residential mobility made along the last five years. Thanks to this study, why the urban areas are currently shrinking can be determined and understood. Indeed, residential mobility, according to urban sociology [START_REF] Authier | Etat des lieux sur les trajectoires résidentielles, PUCA (plan urbanisme construction architecture)[END_REF]) is socially determined and constrained. Numerous determinants combine to explain the households' residential mobility. Social classes backgrounds play a major role: professionals and managerial staff, thanks to their financial and social capital, are more mobile than the working class that values place attachment and domestic capital [START_REF] Renahy | Classes populaires et capital d'autochtonie. Genèse et usage d'une notion[END_REF]. Other social determinants exist, in accordance with an individual lifecycle. They can be linked to professional or family sequences [START_REF] Vignal | Arbitrages résidentiels au sein des couples confrontés à la délocalisation de leur entreprise[END_REF][START_REF] Bonvalet | Le logement, une affaire de famille : l'approche intergénérationnelle des statuts résidentiels[END_REF], to residential projects [START_REF] Bonvalet | Accession à la propriété et trajectoires résidentielles[END_REF][START_REF] Driant | A quoi sert l'accession à la propriété ?[END_REF] and to some key steps in an adult life such as becoming a student or ageing [START_REF] Bozon | L'art et la manière de quitter ses parents[END_REF][START_REF] Christel | Trajectoires résidentielles des personnes âgées[END_REF]. These determinants are intersected with social and financial constraints, which influence access to housing stocks or areas.

In this context, our methodology tries to determine the potential direction of residential mobility from "Migrations communales" files developed by the INSEE to reflect the residential dynamics at the root of the process of urban shrinkage. These files reflect the entire residential mobility of the French population at different times (2, 5, 10 years ...). With this database, people who have recently moved can be studied according to his social class, age, family status and housing. For the purpose of our study, all the inhabitants having moved there less than five years were retained. This period encompasses a wider range in residential mobility, especially in the private rental market the population of which is relatively mobile. The analyses are based on social class, employment status and the household family situation. Two types of analysis were conducted, one for inter regional mobility, the other for internal mobility within the urban area.

Five major categories of interregional residential mobility will be analysed from their main social characteristics (age, marital status, housing, employment, professions):

-Residential mobility potentially related to professional causes; -Residential mobility potentially related to retirement; -Residential mobility potentially related to education; -Residential mobility potentially related to homeownership, at the internal level of the département; -Other migrations the meaning of which is uneasy to classify, including non-active people.

Concerning the residential mobility at the local level, three types of mobility will be classified:

-Residential mobility related to homeownership; -Residential mobility related to social housing; -Residential mobility related to private rental housing.

This method aims at calculating the annual net migration rate in order to determine the main causes of the demographic shrinkage.

To sum up briefly, this methodological framework characterizes the uneven development, which typifies urban shrinkage, the durable breakdown in spatial organization, and has to determine, so as to understand the main causes of this urban shrinkage, the respective roles of suburbanization and deindustrialization especially. Thus, three case studies will be analysed through those two different methods.

CASE STUDIES: MULHOUSE, ROUBAIX AND SAINT-ETIENNE AND THE UNEVEN SHRINKAGE. A PROCESS MAINLY DUE TO DEINDUSTRIALIZATION

Mulhouse, Roubaix and Saint-Etienne: presentation of the case studies

Lille, Mulhouse and Saint-Etienne were industrial boomtowns [START_REF] Duby | Histoire de la France urbaine. t. 4, la ville de l'âge industriel : le cycle haussmannien[END_REF]) in a context of a relatively slow French industrialization in which urban hierarchies had not been disrupted during the nineteenth century. Although their development had been often fluctuating, particularly during times of crisis and war, population growth had been very fast in the nineteenth century. From small towns, the three urban areas became major industrial cities. Population growth was closely linked with industrial development. In Mulhouse, this development was based on cotton mills, then on the mechanical and steel industries because of industrial diversification and finally, on the automotive industry in the industrial decentralization context. Lille was the case of a single textile industrial city specializing in combing and weaving wool. The urban area was also a significant administrative and military hub. Since the 1970's, the development of Lille was driven by services, especially retailing. Saint-Etienne's industrial development was caused both by the establishment of one of the largest firearms factories in France, which caused it to be renamed Armeville during the Napoleonic Empire, but also by sectors such as textiles (ribbons, trimmings), bicycles and mechanics, including machine tools.

Since the 70's, those three cities have experienced a multiform crisis in which employment and population decreased and unemployment, insecure jobs and social deprivation were increasing.

Finally, each city has its own specificities. Mulhouse has the simplest territorial configuration: a mono-centric urban area in the plain next to the Vosges foothills. However, its economic development was been deeply shaped by the development of one of the largest Peugeot factories since 1961. On the contrary, Roubaix has a more complex territorial configuration. A large industrial city, Roubaix developed in the heart of millionaire industrial conurbation, becoming the fifth French urban area in 2010. In this conurbation, Roubaix is, however, the second demographic nucleus after Lille and is almost equal to its immediate neighbour, Tourcoing. The Lille area underwent profound economic restructuring but also a gradual process of metropolization (Paris and Stevens, 2000). Lille is indeed a place concentrating administrative activities because of its regional capital status. Saint-Etienne is a more significant city than the two other case studies. It is also a relatively large administrative centre. It is a set of urban and industrial valleys based on industries in crisis (mining, steel). The industrial crisis affects the whole Saint-Etienne area, which means that it is considered the most emblematic French case of urban shrinkage. The general crisis in Saint Etienne caused rapid urban sprawl, to the extent that C. Cretin (1995) argued "Saint-Etienne is no longer in Saint-Etienne."

An uneven territorial organization

The socio-spatial divisions of Lille, Mulhouse and Saint-Etienne urban areas are relatively strong considering the provincial urban areas with over 200,000 inhabitants. Indeed, the results of the segregation index reveal some traditional socio-spatial divisions in the areas we studied (Fig. 3). According to professions and social categories, the most opposite values relate to executives and workers, each at opposite ends of the social spectrum, while associate professionals, technicians, clerical and commercial employees are relatively mixed in the different neighbourhoods. This finding is consistent with E. Préteceille's study (2003) of the Paris metropolitan area. Considering the intensity of socio-spatial divisions, Saint-Etienne and Mulhouse urban areas are not the most divided ones in France. They are in the average of other French urban areas. Conversely, Lille presents the divisions between PCS among the most intense. The value of the segregation index for the workers is the highest while the executive is the second highest of the 42 provincial urban areas of more than 200 000 inhabitants. This division is partly due to a size effect but also related to the economic profile of the territories: urban areas with the highest values are either industrial urban areas (Le Havre, Dunkirk and Valenciennes) or are the most important French urban areas (Marseilles and Lyons). However, the differentiation between these urban areas and the other French ones is due to the high values of the segregation index for the unemployed. Considering the 45 provincial urban areas, the highest value is reached in Mulhouse; Lille is in the fourth place and Saint-Etienne the seventh. The people affected by unemployment are disproportionately more concentrated in specific neighbourhoods than in any other French urban area. This could be explained by the social consequences of deindustrialization. Considering the results of the hierarchical clustering (Appendix 1), the urban areas are divided in eleven or twelve clusters. Four common categories, divided into subgroups, are clustering the urban areas:

-the areas of suburbanization (QP); -the social housing areas (QLLS); -the ancient inner cities areas (QA); -the mixed areas, at the urban and the social level (QTD); These common clusters are mixed in a spatial patchwork. Within it, neighbourhoods are different in terms of social groups and access to employment, but especially in terms of housing and tenure conditions. That is why the classes are named in relation to morphological characteristics. Before analysing the types of more differentiated neighbourhoods, we must emphasize the importance of the mixed areas. Between 25% and 33% of the population of the three urban areas live in these areas whose main characteristic is to be in average values. Beyond these mixed situations, the strongest socio-spatial differentiation is expressed between, on one hand, areas of suburbanization (QP) and on the other, social housing areas (QLLS) and ancient inner-cities areas (QA).

Areas of suburbanization are composed of single homes in owner-occupancy built since 1949 and mostly since 1975. Working people with low job insecurity populate these areas. Composed of several sub-types, they differ on the greater or lesser presence of managers and professionals and differ on the date of construction. In every studied urban area, one type of these clusters is the most socially exclusive area.

At the opposite of this cluster, there are social housing areas (QLLS) and ancient inner cities' areas (QA). In social housing areas, two subtypes exist. The first consists in large social housing neighbourhoods built between 1949 and 1974 (QLLS1) in which social deprivation is very concentrated. For example, the average unemployment rate is over 25% in every neighbourhood and 48% of the population are working in an insecure job position. In addition, a significant share of immigrants lives there. The second type (QLLS2) is composed of newer apartments, built between 1974 and 1990. In this type, the social deprivation is not as significant and the clerical and commercial employees, technicians and associate professionals are more represented.

Finally, the ancient inner cities (QA) are composed of private dwellings built before 1949 characterized by a poor amenity level. Despite their urban structure differences with social housing areas, they have a very similar social profile. A high level of insecure jobs, a high unemployment rate and an overrepresentation of immigrants characterize them. In every studied urban area, one of the three existing types is the most deprived area. The main characteristics of this sub-cluster are the highest level of squalor, the highest unemployment rate, the highest concentration of manual workers and immigrants. In this context, some ancient inner cities are facing a more important social crisis than the traditional French areas of social crisis, the social housing areas.

Beyond these common characteristics, the urban areas present some territorial and social differences. In the Mulhouse urban area, the social crisis is concentrated in the core-city, especially in the ancient inner city and social housing areas. Conversely, the suburbs are very slightly characterized by social deprivation, unemployment and job insecurity. In the Lille conurbation, the industrial cities are mainly socially deprived. In this context, Roubaix is the most marginalized and deprived: 72% of the population is living in ancient inner cities and social housing areas, areas characterized by poor social conditions. However, Lille presents a changing social profile: more and more professionals and managerial staff are living in the city-centre (QCA), to the extent that they are as many as in the suburbs. The social crisis in the Saint-Etienne urban area is more scattered, affecting all the urban valleys. The Saint-Etienne inner city is both more socially diverse and characterized by job insecurity and unemployment than the rest of the area.

As a preliminary conclusion, the segregation index and hierarchical clustering analyses have demonstrated that the three urban areas are socio-spatially divided. The working class and the unemployed mostly suffer from these divisions and live in poor social conditions in social housing areas or in downgraded inner cities. Thus, an uneven development characterizes these three cities and urban areas.

Development courses: shrinkage as an uneven social and spatial dynamic

Judging by the definition of urban shrinkage, the three studied areas seem impacted by this process. First, the three cities are experiencing a strong deindustrialization. Thus, each of the three cities is undergoing a long-term process of decline of factory employment and production. Since 1968, 11 000 workers' jobs were obliterated in the Mulhouse urban area, 87% concentrated in Mulhouse city. In the Lille urban area, 90 000 workers jobs have gone since 1968, the city of Roubaix concentrating 30% of these losses. In Saint-Etienne, the decline of factory employment is 41,000 jobs, 67% concentrated in Saint-Etienne itself. In addition, the total number of jobs is lower than in 1968 in all three cities. However, a moderate employment growth is still observed at the urban area scale, illustrating an unequal spatial reorganization within. A process of uneven economic development is emerging: the territories outside the city centres are more resilient, growth trajectories, while the central cities are facing a relative and absolute employment shrinkage. Therefore, the central cities seem to be the deindustrialization losers and see their roles in the local economic organization change. This uneven development reveals a suburbanization process in activities.

In this context, three types of suburbanization in activities are represented in each case study. The Mulhouse urban area is the strongest one, economically speaking, despite a strong suburbanization in activities and a severe deindustrialization in the city centre. While the core city is in absolute decline in terms of total employment, it tends to capture the quaternary activities (managerial jobs and associate professionals). This job loss is primarily related to deindustrialization in former industrial and working-class neighbourhoods. On the contrary, the immediate suburban rings and suburban areas show a strong increase in all types of jobs, especially services jobs for the population (retail, education, health...). In these territories, industry also resists more strongly.

The Lille metropolitan area has a different spatial configuration. It is the urban area with the fastest internal reorganization. Indeed, two periods clearly stand out. Between 1968 and 1990, the major industrial centres (Roubaix, Tourcoing) and little industrial towns experienced an absolute decline in employment, mainly due to deindustrialization. During this period, the suburban areas, including the new town of Villeneuve d'Ascq, witnessed a rapid employment growth. In this context, top service jobs, related to the location of headquarters and the development of universities, are concentrating in Villeneuve d'Ascq. Lille, meanwhile, suffered a rapid and brutal deindustrialization, hardly compensated by the development of educational, cultural, health and retail services. Since 1990, the major industrial centres have been decreasing at a slower pace. Suburban rings are growing faster, especially Villeneuve d'Ascq, which has become the second employment centre in the metropolitan area. Conversely, Lille experienced an exponential employment growth, still in the cultural, the educational and the health services but also, which is new, in the highest managerial and BtoB services. This growth exemplifies the success of local metropolization policies for Lille, like the Euralille project. However, the declining economic situation in other traditional industrial cities remains unchanged. Thus, economic contrasts are being reinforced and are changing over time.

In the Saint-Etienne urban area, deindustrialization was a severe and intense process, especially in its heart, ,Saint-Etienne and Gier and Ondaine urban valleys. Only the distant suburban areas are growing, e.g., the Loire-Forez Plain. Total employment declined in Saint-Etienne, in the valleys of Gier and Ondaine. These three territories continuously lost jobs from 1975 to 1999, and mostly in the heart of the inner city, Saint-Etienne. In contrast, employment in the immediate suburban rings doubled. In this context, it is both an obvious process of deindustrialization and suburbanization in activities that the Saint-Etienne urban area experienced. The process of deindustrialization is obvious in Saint-Etienne and in both urban valleys: the manufacturing function collapsed, likewise worker employment. The growth of other economic functions is slow, such as in business services or education, health and culture. Thus BtoB services decreased in Gier valley and grew slowly in Saint-Etienne and Ondaine. Retail functions, health and education are growing but relatively slowly. Thus, between 1968 and 2007, Saint-Etienne lost its economic specialization profile in upper metropolitan functions and in services. Conversely, employment, be it labour or business or people services, has grown rapidly in both the distant suburbs.

Beyond this analysis of the internal process of economic shrinkage, deindustrialization results in a marginalization of their economic situation in the French context. Compared to urban areas with over 200,000 inhabitants, the three cities show a lack of economic change, reversing to the theory of deindustrialization as a process of upgrading to tertiary economy (Fig. 4). Indeed, the manufacturing function remains central while BtoB services and upper metropolitan functions are lagging behind average situations in French urban areas. Thus, this table shows the relative marginalization of the three cities, particularly noticeable in Mulhouse and Saint-Etienne, in terms of strategic economic functions, of BtoB services, although Lille area has seen a slight process of economic metropolization. Legend: when the specialization index is less than 1, the result indicates that the share of the economic function is less important than in other urban areas with over 200,000 inhabitants. Conversely, when the result is greater than 1, the share of the economic function is greater than in other urban areas with over 200,000 inhabitants. When the specialization index is blue, it reflects an under-specialization compared to urban centres with more than 200 000 and when it appears in red, it indicates an over-representation.

Source : INSEE, Données sur les fonctions économiques des zones d'emplois entre 1982 et 2007

On the social level, the Mulhouse, Roubaix and Saint-Etienne urban areas have a cumulative social crisis characterized by an unemployment outburst and job insecurity. Thus, the social consequences of deindustrialization are plenty. Indeed, compared to other French urban areas, each of the three urban areas has particular features. First, compared to the French urban centres, the central cities of Mulhouse, Roubaix and Saint-Etienne are more characterized by working class, despite the unprecedented industrial collapse. In contrast, the relative proportion of executives and middle management decreases to the extent that sub-specialization statistics emerge in Mulhouse and Roubaix for technicians and associate professions. However, the most striking feature is the extreme unemployment increase. Second, the urban areas face a similar situation but to a lesser degree given those of inner cities. Working class, unemployment (which is rapidly increasing), and the decreasing share of executives and intermediate occupations characterize those urban areas. Third, despite particular situations among the three urban areas, manual workers and unemployed people are gradually concentrated in central cities and the share of executives and associate professions is falling rapidly to the extent that they are underrepresented in Mulhouse and Roubaix. Legend: when the specialization index is less than 1, the result indicates that the share of the profession and the social category is less important than in the other urban areas with over 200,000 inhabitants. Conversely, when the result is greater than 1, the share of the profession and the social category is greater than in the other urban areas with over 200,000 inhabitants. When the specialization index is blue, it reflects an under-specialization compared to urban centres with more than 200 000 and when it appears in red, it indicates an over-representation. Along with this difficult economic and social situation, the three inner cities are experiencing a declining demography. Thus, Mulhouse, Roubaix and Saint-Etienne have lost between 5% and 25% of their populations since 1968. This loss of residents in central cities may be partly explained by a suburbanization process. Indeed, suburban areas are experiencing a strong population growth since 1968. This growth is based on a major suburban residential development. The number of homes built has doubled in suburban spaces; meanwhile the number of homes has decreased in Saint-Etienne and Roubaix. Thus, suburbanization is playing a role in the demographic shrinkage of central cities. However, it does not fully explain the demographic decline. Indeed, the urban areas are experiencing rather slower population growth trends than in France. If the Mulhouse urban area grows along with national trends, the Lille urban area is way below the average and the Saint-Etienne urban area is almost nought. Then, at both the urban area and inner-city scales, a negative net migration has deepened since the 70's. While in the 70's the negative net migration affected only central cities, it has gradually affected the first suburban rings since the 1990s. In each urban area, the closest suburban areas of inner cities are growing only thanks to an important natural increase. For instance, in the case of Lille since 1990, no part of the urban area is experiencing a positive net migration, despite the continuation of a process of suburbanization.

Given this analysis of the migration balance, two preliminary conclusions could be drawn. First, suburbanization is not an urban growth process but a restructuration of the residential geography of urban areas. Second, the inner city demographic shrinkage is not only due to suburbanization. It is also a loss of residential attractiveness concerning the whole urban area. To confront this dual process, we now aim at explaining the main causes of demographic shrinkage. Given the residential mobility, a demographic and unbalanced migration process, between city centres and their suburban areas and between city centres and the regional and national scales, explains city centres' shrinkage.

Indeed, the unbalanced migration between central cities and the regional and national levels explains between one-third and half of negative migration balance. This imbalance is mainly due to professional migrations, which account for more than a half, i.e. between -1,800 and -2,700 people in five years. Professional migrations are the feature of working people and mainly upper classes (professionals and managerial staff, associate professionals and technicians), according to the literature on long-distance residential mobility (Baccaïni, 2007;Collet et al., 2011aCollet et al., , 2011b)). However, more astonishingly, clerical and manual workers also take a big part in this type of residential mobility. This could reflect consequences of deindustrialization: the worsening economic situation shrinks the job market for working classes. Facing less and less job opportunities, a significant part of them are leaving urban areas to find jobs and restore their social careers. This residential imbalance is also explained by homeownership in the distant suburban fringes, i.e. between 25% and 33% of the imbalance, between -1,600 and -550 people in five years. The main social profile is constituted of young working families. No social categories are overrepresented: both working and upper classes are well represented in this process.

Therefore, this supra-local residential imbalance is socially selective because it is the young and graduated people, the upper classes, the working people and the socially integrated families who are leaving the city centres. Thus, it reinforces the socio-spatial contrasts and the social profile of inner cities because it produces a socially selective demographic shrinkage and blocks the social change in the workforce by leaving the most deprived people in city centres and in selecting those who are the best integrated.

At a local level, homeownership plays a major role in the explanation of the residential imbalance. This is the most important cause of demographic shrinkage. In five years, the imbalance is about -1,800 and -3,200 people. Between 66% and 80% of the imbalance is explained by this residential career. This reflects some upward residential logics, closely correlated with suburbanization. If the upper classes are overrepresented, the working classes are also many in contributing to these residential trajectories. Indeed, in every city, working classes are more numerous than upper classes in leaving the city centres. In five years, between -1,500 and -1,900 people belonging to working classes take up homeownership. However, the main difference between the working classes leaving the city centres and the others is the fact that these working classes are settled families in which the two adults are mostly at work: the social profile of the people involved in homeownership trajectories is 90% working families.

This residential career reflects a place-attachment problem for working classes when they are stabilized. Given these results, the city centres seem repulsive for each population segment. Only the most deprived people, who can't leave the city centres because they don't dispose of social and economic resources to take part in residential mobility, reside in city centres. In this context, this residential career plays a major role in the increasingly uneven development in the urban area by a socially selective process of demographic shrinkage in city centres. Segregation is increasing within the urban area: the social differences between central and suburban areas are deepening with the most integrated people in suburbs and the most deprived in city centres.

CONCLUSIONS

To sum up, Mulhouse, Roubaix and Saint-Etienne are experiencing a long-term economic, social and demographic weakening situation. A dual process of loss of attractiveness drives this: a demographic and an economic one. However, the two factors do not play the same role. The loss of economic attractiveness seems to be the central factor in the shrinkage process in the traditional industrial urban areas. Indeed, an obvious deindustrialization process, resulting from globalization, characterizes these territories. In every case study, the traditional and industrial economic base collapsed although dependence on traditional industries remains. There is no real transformation of economic production, except in the case of Lille. This economic crisis produces a cumulative social crisis in working classes, with few changes in social structures of working populations. The social structure of the industrial city continues, even more strongly in city centres. Moreover, these cities face a loss of residential attractiveness in which suburbanization is paradoxical and gives rise to the demographic shrinkage of inner cities.

Spatially, these combined changes result in a spatial model for the shrinking urban area: the shrinking urban area is socio-spatially divided between, on one side, the inner cities which concentrate social difficulties and a declining economic and demographic situation; and on the other, the suburbs characterized by growth which concentrate the most affluent and the integrated populations, the new jobs and the new economic development areas. In one respect, the city centres are cumulatively losers in this uneven process: these are the areas where population loss is the fastest, deindustrialization the most severe and social concentration of deprived people the most intense. In another, driven by a process of suburbanization, the suburbs are growing into resilient areas, characterized by an economic, industrial and demographic strength and an immunity to social problems.

Through the dynamic analysis of urban shrinkage caused in these traditional industrial urban areas, one can consider that the loss of economic attractiveness and deindustrialisation are the heart of all other weakening processes. It is deindustrialization and the loss of economic attractiveness that has led to the loss of residential attractiveness. Indeed, deindustrialization causes a major social crisis, selectively impacting the working classes. Deindustrialization and the worsening economic situation are nurturing demographic shrinkage at the supralocal level that is primarily explained by professional migrations. Young qualified people, upper-classes and working classes are leaving cities for other regions in France because of dwindling job opportunities. This process explains between 16.5 and 25% of the negative migration balance. The second explanation is linked with the suburbanization process and homeownership. Between 25% and 40% of negative migration balance is explained by this residential career. Suburbanization could be considered as a different process; however, given the social profile of the population, it seems to be activated by this declining economic situation. Indeed, suburbanization is closely related to homeownership, which is primarily carried out by working households, regardless of their social class. Not a single social category is clearly overrepresented. Moreover, working classes are more engaged in this homeownership course than upper classes. Suburbanization, linked with homeownership, is an upward residential logic, closely related to working and settled families. The main difference between the population living in city centres and the population leaving the cities is the fact that the latter enjoy a livelihood. This loss of economic attractiveness leads to deindustrialization, which worsens territorial divisions and causes economic, social and demographic crises. It is magnified by a loss of local residential attractiveness, born in a spatially uneven and socially selective suburbanization process. These two intermingled processes are creating a forceful intraurban differentiation within urban areas: located side-by-side are some growth islands, socially integrated and economically strong and shrinking areas in which difficulties are all piled up and self-sustained. Facing the major role of deindustrialization in the explanation of urban shrinkage in traditional industrial urban areas, this paper raises the question of

Figure 1 :

 1 Figure 1: Data analysed for hierarchical clustering

Figure 2 :

 2 Figure 2: Main results of the segregation index method

Figure 3 :

 3 Figure 3: Specialization index of the economy for Mulhouse, Lille and Saint-Etienne compared to the major French urban areas from 1968 to 2007.

Figure 4 :

 4 Figure 4: Specialization index of professions and social categories for the major French urban areas from 1968 to 2007.

Figure 5 :

 5 Figure 5: Unemployment rate in inner cities and suburban rings (2007)

Figure 6 :

 6 Figure 6: Demographic change in the urban areas since 1968

Figure 7 :

 7 Figure 7: Migration balance by residential mobility

Figure 8 :

 8 Figure 8: Migration index by professions and social categories per annum

1

  People with an insecure jobs Rate of the workforce employed with an insecure jobs (temporary jobs, part-time jobs) 2 Unemployed workforce Rate of the workforce unemployed 3 Professional and managerial Rate of professional and managerial in the workforce 4 Associate professional and technicians Rate of associate professional and technicians in the workforce 5 Clerical workers and sale workers

	Social datas
	Housing datas
	4 House 49	Rate of houses built before 1949 in the occupied homes
	5 House 4974	Rate of houses built between 1949 and 1974 in the occupied homes
	6 House 7490	Rate of houses built between 1974 and 1990 in the occupied homes
	7 House 90+	Rate of houses built after 1990 in the occupied homes
	8 Flat 49	Rate of flats built before 1949 in the occupied homes
	9 Flat 4974	Rate of flats built between 1949 and 1974 in the occupied homes
	10 Flat 7490	Rate of flats built between 1974 and 1990 in the occupied homes
	11 Flat 90+	Rate of flats built after 1990 in the occupied homes
	12 Uncomfortable homes	Rate of uncomfortable homes in the occupied homes (without bathroom or heating)

Rate of clerical or sale workers in the workforce 6 Manual workers Rate of manual workers in the workforce 7 Foreign residents Rate of foreign native in the total population 1 Homeowners Rate of homeonwers in the total population (household) 2 Social renters Rate of social renters in the total population (household) 3 Private housing renters Rate of private housing renters in the total population (household)

Statistical areas of

2,000 inhabitants.


Made Windu

Antara Kesiman

Sophea Prum
email: sopheaprum@gmail.com

Jean-Christophe Burie
email: jcburie@univ-lr.fr

Jean-Marc Ogier
email: jean-marc.ogier@univ-lr.fr

Study on Feature Extraction Methods for Character Recognition of Balinese Script on Palm Leaf Manuscript Images

Keywords: feature extraction, character recognition, Balinese script, palm leaf manuscript, performance evaluation I

The complexity of Balinese script and the poor quality of palm leaf manuscripts provide a new challenge for testing and evaluation of robustness of feature extraction methods for character recognition. With the aim of finding the combination of feature extraction methods for character recognition of Balinese script, we present, in this paper, our experimental study on feature extraction methods for character recognition on palm leaf manuscripts. We investigated and evaluated the performance of 10 feature extraction methods and we proposed the proper and robust combination of feature extraction methods to increase the recognition rate.

INTRODUCTION

Isolated handwritten character recognition (IHCR) has been the subject of intensive research during the last three decades. Some IHCR methods have reached a satisfactory performance especially for Latin script. However, development of IHCR methods for other various new scripts remains a major challenge for researchers. For example, the IHCR challenges for historical documents discovered in the palm leaf manuscripts, written using the script originating from the region of Southeast Asia, such as Indonesia, Cambodia, and Thailand. Ancient palm leaf manuscripts from Southeast Asia have received great attention from historian and also researchers in the field of document image analysis, for example some works on document analysis of palm leaf manuscript from Thailand [START_REF] Chamchong | Comparing Binarisation Techniques for the Processing of Ancient Manuscripts[END_REF][START_REF] Fung | A Review of Evaluation of Optimal Binarization Technique for Character Segmentation in Historical Manuscripts[END_REF] and from Indonesia [START_REF] Kesiman | An Initial Study On The Construction Of Ground Truth Binarized Images Of Ancient Palm Leaf Manuscripts[END_REF][START_REF] Kesiman | An Analysis of Ground Truth Binarized Image Variability of Palm Leaf Manuscripts[END_REF]. The physical condition of the palm leaf documents is usually degraded. Therefore, researchers have to deal at the same time with the digitization process of palm leaf manuscripts and with the automatic analysis and indexing system of the manuscripts. The main objective is to bring additional value to the digitized palm leaf manuscripts by developing tools to analyze, to index and to access quickly and efficiently to the content of the manuscripts. It tries to make the manuscripts more accessible, readable and understandable to a wider audience and to scholars all over the world.

The majority of Balinese have never read any palm leaf manuscript because of language obstacles as well as tradition which perceived them as sacrilege. They cannot access to the important information and knowledge in palm leaf manuscript. Therefore, using an IHCR system will help to transcript these ancient documents and translate them to the current language. IHCR system is one of the most demanding systems which has to be developed for the collection of palm leaf manuscript images. Usually, an IHCR system consists of two main steps: feature extraction and classification. The performance of an IHCR system greatly depends on the feature extraction step. The goal of feature extraction is to extract information from raw data which is most suitable for classification purpose [START_REF] Aggarwal | Use of Gradient Technique for Extracting Features from Handwritten Gurmukhi Characters and Numerals[END_REF]. Many feature extraction methods have been proposed to perform the character recognition task [START_REF] Aggarwal | Use of Gradient Technique for Extracting Features from Handwritten Gurmukhi Characters and Numerals[END_REF][START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF][START_REF] Arica | Optical character recognition for cursive handwriting[END_REF][START_REF] Blumenstein | A novel feature extraction technique for the recognition of segmented handwritten characters[END_REF][START_REF] Bokser | Omnidocument technologies[END_REF][START_REF] Zahid Hossain | Rapid Feature Extraction for Optical Character Recognition[END_REF][START_REF] Fujisawa | Handwritten numeral recognition using gradient and curvature of gray scale image[END_REF][START_REF] Jin | SSIFT: An Improved SIFT Descriptor for Chinese Character Recognition in Complex Images[END_REF][START_REF] Rani | An Efficient Feature Extraction Method for Handwritten Character Recognition[END_REF]. These methods have been successfully implemented and evaluated for recognition of Latin, Chinese and Japanese characters as well as digit recognition. However, only a few system are available in the literature for other Asian scripts recognition. For example, some of the works are for Devanagari script [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF][START_REF] Ramteke | Invariant Moments Based Feature Extraction for Handwritten Devanagari Vowels Recognition[END_REF], Gurmukhi script [START_REF] Aggarwal | Use of Gradient Technique for Extracting Features from Handwritten Gurmukhi Characters and Numerals[END_REF][START_REF] Lehal | A Gurmukhi script recognition system[END_REF][START_REF] Sharma | Recognition of Isolated Handwritten Characters in Gurmukhi Script[END_REF][START_REF] Singh Siddharth | Handwritten Gurmukhi Numeral Recognition using Different Feature Sets[END_REF], Bangla script [START_REF] Zahid Hossain | Rapid Feature Extraction for Optical Character Recognition[END_REF], and Malayalam script [START_REF] Deepa | Feature Extraction Techniques for Recognition of Malayalam Handwritten Characters: Review[END_REF]. Those documents with different scripts and languages surely provide some real challenges, not only because of the different shapes of characters, but also because the writing style for each script differs: shape of the characters, character positions, separation or connection between the characters in a text line.

Choosing efficient and robust feature extraction methods plays a very important role to achieve high recognition performance in an IHCR and OCR [START_REF] Aggarwal | Use of Gradient Technique for Extracting Features from Handwritten Gurmukhi Characters and Numerals[END_REF]. The performance of the system depends on a proper feature extraction and a correct classifier selection [START_REF] Zahid Hossain | Rapid Feature Extraction for Optical Character Recognition[END_REF]. With the aim of developing an IHCR system for Balinese script on palm leaf manuscript images, we present, in this paper, our experimental study on feature extraction methods for character recognition of Balinese script. It is experimentally reported that to improve the performance of an IHCR system, the combination of multi features is recommended [START_REF] Trier | Feature extraction methods for character recognition-A survey[END_REF]. Our objective is to find the combination of feature extraction methods to recognize the isolated characters of Balinese scripts on palm leaf manuscript images. We investigated 10 feature extraction methods and based on our preliminary experiment results, we proposed the combination of feature extraction methods to improve the recognition rate. Two classifiers: k-NN (k-Nearest Neighbor) and SVM (Support Vector Machine) are used in our experiments. We also compared the results with a convolutional neural network based system. In such a system, feature extraction is not required.

This paper is organized as follow: section II gives a brief description about Balinese script on the collection of palm leaf manuscripts in Bali and the challenges for IHCR. Section III presents the brief description of feature extraction methods and our proposed combination of features which were evaluated in our experimental study. The palm leaf manuscript image dataset used in this work and the experimental results are presented in Section IV. Conclusions with some prospects for the future works are given in Section V.

BALINESE SCRIPT ON PALM LEAF MANUSCRIPTS AND THE CHALLENGES FOR IHCR

A. The Balinese Script on Palm Leaf Manuscripts

The Balinese manuscripts were mostly recorded on dried and treated palm leaves, called Lontar. They include texts on issues such as religion, holy formulae, rituals, family genealogies, law codes, treaties on medicine, arts and calendars, prose, poems and even magics. Unfortunately, many discovered Lontars, part of the collections of some museums or belonging to private families, are in a state of disrepair due to age and due to inadequate storage conditions. Lontars were written and inscribed with a small knife-like pen (a special tool called Pengerupak). It is made of iron, with its tip sharpened in a triangular shape so it can make both thick and thin inscriptions. The manuscripts were then scrubbed by the natural dyes to leave a black color on the scratched part as text. The Balinese palm leaf manuscripts were written in Balinese script and in Balinese language. Writing in Balinese script, there is no space between words in a text line. Some characters are written on upper baseline or under the baseline of text line. Lontars were written in the ancient literary texts composed in the old Javanese language of Kawi and Sanskrit.

B. The IHCR Challenge for Balinese Script in Palm Leaf

Manuscripts For Balinese IHCR, there are 2 challenges: 1) the complexity of Balinese script, and 2) the poor quality of palm leaf manuscripts. Balinese script is considered to be one of the more complex scripts from Southeast Asia. The alphabet and numeral of Balinese script is composed of ±100 character classes including consonants, vowels, diacritics, and some other special compound characters. Usually, palm leaf manuscripts have poor quality since the documents are degraded over time due to storage conditions. The palm leaf manuscripts contain discolored parts and artefacts due to aging. They have low intensity, variations or poor contrast, random noises, and fading [START_REF] Kesiman | An Analysis of Ground Truth Binarized Image Variability of Palm Leaf Manuscripts[END_REF]. Several deformations in the character shapes are visible due to the merges and fractures of the characters, varying space between letters, and varying space between lines (see Fig. 1). It is known that the similarities of distinct character shapes, the overlaps, and interconnection of the neighboring characters further complicate the problem of an OCR system [START_REF] Arica | Optical character recognition for cursive handwriting[END_REF]. One of the main problem faced when dealing with segmented handwritten character recognition is the ambiguity and illegibility of the characters [START_REF] Blumenstein | A novel feature extraction technique for the recognition of segmented handwritten characters[END_REF]. In the scope of this paper, we will focus on scripts written on palm leaf whose characters are even more elaborated in ancient written form. These characteristics provide a suitable challenge for testing and evaluation of robustness for feature extraction methods which were already proposed for character recognition. Balinese scripts on palm leaf manuscripts offer a real new challenge in IHCR development.

III. FEATURE EXTRACTION METHODS AND OUR PROPOSED COMBINATION OF FEATURES

Many feature extractions methods have been presented in the literature. Each method has its own advantages or disadvantages over other methods. In addition, each method may be specifically designed for some specific problem. Most of feature extraction methods, extract the information from binary image or grayscale image [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF]. Some surveys and reviews on features extraction methods for character recognition were already reported [START_REF] Trier | Feature extraction methods for character recognition-A survey[END_REF][START_REF] Kumar | Study of Features for Hand-printed Recognition[END_REF][START_REF] Neha | A Review on Feature Extraction Techniques for Optical Character Recognition[END_REF][START_REF] Pal | Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques[END_REF][START_REF] Pal | Indian script character recognition: a survey[END_REF][START_REF] Govindan | Character recognition -A review[END_REF]. In this work, first, we investigated and evaluated some most commonly used features for character recognition: histogram projection [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF][START_REF] Zahid Hossain | Rapid Feature Extraction for Optical Character Recognition[END_REF][START_REF] Deepa | Feature Extraction Techniques for Recognition of Malayalam Handwritten Characters: Review[END_REF], celled projection [START_REF] Zahid Hossain | Rapid Feature Extraction for Optical Character Recognition[END_REF], distance profile [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF][START_REF] Deepa | Feature Extraction Techniques for Recognition of Malayalam Handwritten Characters: Review[END_REF], crossing [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF][START_REF] Zahid Hossain | Rapid Feature Extraction for Optical Character Recognition[END_REF], zoning [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF][START_REF] Blumenstein | A novel feature extraction technique for the recognition of segmented handwritten characters[END_REF][START_REF] Bokser | Omnidocument technologies[END_REF][START_REF] Deepa | Feature Extraction Techniques for Recognition of Malayalam Handwritten Characters: Review[END_REF], moments [START_REF] Ramteke | Invariant Moments Based Feature Extraction for Handwritten Devanagari Vowels Recognition[END_REF][START_REF] Deepa | Feature Extraction Techniques for Recognition of Malayalam Handwritten Characters: Review[END_REF], some directional gradient based features [START_REF] Aggarwal | Use of Gradient Technique for Extracting Features from Handwritten Gurmukhi Characters and Numerals[END_REF][START_REF] Fujisawa | Handwritten numeral recognition using gradient and curvature of gray scale image[END_REF], Kirsch Directional Edges [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF], and Neighborhood Pixels Weights (NPW) [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF]. Secondly, based on our preliminary experiment results, we proposed and evaluated the combination of NPW features applied on Kirsch Directional Edges images, with Histogram of Gradient (HoG) features and zoning method. This section will only briefly describe the feature extraction methods which were used in our proposed combination of features and the convolutional neural network. For more detail description of other commonly used feature extraction methods which were also evaluated in this experimental study, please refer to references mentioned above.

A. Kirsch Directional Edges

Kirsch edges is a non-linear edge enhancement [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF]. Let Ai (i=0,1,2,...,7) be the eight neighbors of the pixel (x,y), i is taken as modulo 8, starting from top left pixel at the moving clock-wise direction.

Four directional edge images are generated (Fig. 2) by computing the edge strength at pixel (x,y) in four (horizontal, vertical, left diagonal, right diagonal) direction, defined as GH, GV, GL, GR, respectively [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF]. They can be denoted as bellow:

GH(x,y) = max( | 5S0 -3T0 | , | 5S4 -3T4 | ) (1) GV(x,y) = max( | 5S2 -3T2 | , | 5S6 -3T6 | ) (2) GR(x,y) = max( | 5S1 -3T1 | , | 5S5 -3T5 | ) (3) GL(x,y) = max( | 5S3 -3T3 | , | 5S7 -3T7 | ) (4)
Where Si and Ti can be computed by: Si = Ai + Ai+1 + Ai+2 (5) Ti = Ai+3 + Ai+4 + Ai+5 + Ai+6 + Ai+7 [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF] Each directional edge image is thresholded to produce a binary edge image. The binary edge image is then partitioned into N smaller regions. Then, edge pixel frequency in each region is computed to produce the feature vector. In our experiments, we computed Kirsch feature from grey scale image with 25 smaller regions to produce a 100 dimensions feature vector. Based on the empirical tests for our dataset, the Kirsch edge image can be optimally thresholded with a threshold value of 128. The feature value is then normalized by the maximum value of edge pixel frequency from all regions. Fig. 2 Four directional Kirsch edge images

B. Neighborhood Pixels Weights (NPW)

Neighborhood Pixels Weight (NPW) was proposed by Satish Kumar [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF]. This feature may work on binary as well as on gray images. NPW considers four corners of neighborhood for each pixel: top left, top right, bottom left, and bottom right corner. The number of neighbors considered on each corner is defined by value of layer level (see Fig. 3). Level 1 considers only pixels in layer 1 on each corner (1 pixel), level 2 considers pixels in layer 1 and 2 (4 pixels), and level 3 considers pixels in all layers (9 pixels). In the case of binary image, the weight value on each corner is obtained by counting the number of pixel character, divided by total number of neighborhood pixels on that corner. For grayscale image, the weight value on each corner is obtained by summing the gray level of all neighborhood pixels, divided by maximum possible weight due to all neighborhood pixels on that corner (nb_neighborhood_pixels x 255). Four weighted plans are constructed for each corner from the weighted value of all pixels on the image. Each plane is divided into N smaller regions, and the average weight of each region is computed. The feature vector is finally constructed from the average weight of each region from each plane (N x 4 vector dimension).

In our experiments, we computed NPW features in level 3 neighborhood with 25 smaller regions (N=25) to produce a 100 dimensions feature vector. The feature value is normalized by the maximum value of average weight from all regions. We tested the performance of NPW feature for both binary and grayscale image.

C. Histogram of Gradient (HoG)

The gradient is a vector quantity comprising of magnitude as well as directional component computed by applying its derivatives in both horizontal and vertical directions [START_REF] Aggarwal | Use of Gradient Technique for Extracting Features from Handwritten Gurmukhi Characters and Numerals[END_REF]. The gradient of an image can be computed either by using for example Sobel, Roberts or Prewitt operator. The gradient strength and direction can be computed from the gradient vector. Gradient feature vector used in [START_REF] Aggarwal | Use of Gradient Technique for Extracting Features from Handwritten Gurmukhi Characters and Numerals[END_REF] is formed by accumulating the gradient strength separately along different directions.

To compute HoG, first, we compute the gradient magnitude and gradient direction of each pixel of the input image. The gradient image is then divided into some smaller cells, and in each cell we generate the histogram of directed gradient by assigning the gradient direction of each pixel into certain range of orientation bin which are evenly spread over 0 to 180 degrees or 0 to 360 degrees (Fig. 4 &5). The histogram cells are then normalized with a larger overlap connected blocks.

Fig. 4 An image with 4x4 oriented histogram cells and 2x2 descriptor blocks overlapped on 2x1 cells

The final HoG descriptor is then generated from all concatenated vector of the histogram after the block normalization process. For our experiments, we used the HoG implementation of VLFeat1 . We computed HoG feature from gray scale image with cell size of 6 pixels and with 9 different orientations to produce a 1984 dimensions feature vector.

Fig. 5 The representation of the array of cells HoG

D. Zoning

Zoning is computed by dividing the image into N smaller zones: vertical, horizontal, square, diagonal left and right, radial or circular zone (see Fig. 6). The local properties of image are extracted on each zone. Zoning can be implemented for binary image and grayscale image [START_REF] Kumar | Neighborhood Pixels Weights-A New Feature Extractor[END_REF]. For example, in binary image, the percentage density of character pixels in each zone is computed as local feature [START_REF] Bokser | Omnidocument technologies[END_REF]. In grayscale image, the average of gray value in each zone is considered as local feature [START_REF] Deepa | Feature Extraction Techniques for Recognition of Malayalam Handwritten Characters: Review[END_REF]. Zoning can be easily combined with other feature extraction methods [START_REF] Zahid Hossain | Rapid Feature Extraction for Optical Character Recognition[END_REF], for example in [START_REF] Blumenstein | A novel feature extraction technique for the recognition of segmented handwritten characters[END_REF]. In our experiments, we computed zoning with 7 zone types (zone width or zone size = 5 pixels) and combined them into a 205 dimensions feature vector. We also tested the zoning feature on the image of the skeleton.

E. Our proposed combination of features

After evaluating the performance of 10 individual feature extraction methods, we found that the HoG features, NPW features, Kirsch features and Zoning method give a very promising result (see Table I in Section IV). We obtained 62,45% of recognition rate only by using Kirsch features. It means that the four directional Kirsch edge images already serve as a good feature discriminants for our dataset. The shape of Balinese characters are naturally composed by some curves. We can notice that Kirsch edge image is to give the initial directional curve features for each character. On the other hand, NPW features have an advantage that it can be applied directly to gray level images. Our hypothesis is the four directional Kirsch edge images will provide a better feature discriminants for NPW features. Based on this hypothesis, we proposed a new feature extraction method by applying NPW on kirsch edge images. We call this new method as NPW-Kirsch (see Fig. 7). Finally, we concatenate NPW-Kirsch with two other features, HoG and Zoning method.

F. Convolutional neural network

In this experiment, we also use convolutional neural network where feature extraction step is not required. The network considers the value of each pixel of the input image as the input layer. In this experiment, we used Tensorflow library 2 .

More specifically, multilayer convolutional neural network is used. The architecture of our network is illustrated in Fig. 8. Given an input grayscale image of 28x28 pixels, the first convolutional layer (C1) is computed by using a sliding window of 5x5 pixels. We obtain C1 layer containing 28x28x32 neurons, where 32 is the number of features maps chosen. For each sliding window on the neuron (i, j), the convolutional C(i, j) output can be computed by: 4

k l i k j l k l C i j W I       4 ( , ) ( , ) 0 0 ( , ) ( )
Where W is a 5x5 matrix to be used as the shared weights matrix, and I is the input neurons. Rectified linear unit is then applied. We obtain: C=ReLu(b+C), where b is the bias. Then, we apply max-pooling using a window of 2x2 which consists in choosing the maximum activation in the selected region as the output. We obtain P2 layer consists of 14x14x32 neurons. After computing the second convolutional layer (C3) and second max-pooling (P4), we obtain a layer (P4) of 7x7x64 neurons. We add a fully-connected layer (F5) of 1024 neurons, where

P'4 is a one dimension matrix containing the 3136 neurons in P4. W4 is a 3136x1024 shared weight matrix, and b4 is the bias.

Finally, we fully connect this F5 layer to the output layer, where the number of neurons equals the number of classes by using the equation 8. We present our experimental study on feature extraction methods for character recognition of Balinese script on palm leaf manuscripts.

A. Palm leaf manuscript images dataset

Our Balinese palm leaf manuscript images dataset come from 5 different locations in Bali, Indonesia: 2 museums and 3 private families. In order to obtain the variety of the manuscript images, the sample images used in this experiment are randomly selected from 23 different collections (contents), with the total of 393 pages. By using the collection of wordlevel annotated patch images from our palm leaf manuscript collection, which were produced manually using Aletheia 3 [START_REF] Clausner | Aletheia -An Advanced Document Layout and Text Ground-Truthing System for Production Environments[END_REF] in our previous ground truthing process, we collected our isolated Balinese character dataset. We applied binarization process to automatically extract all connected component found on the word patch images. Our Balinese philologists manually annotated the segment of connected component that represent a correct character in Balinese script. All patch images that have been segmented and annotated at character level will serve as our isolated character dataset. It consists of 133 character classes, with a total number of 19.,383 character samples and 11.,710 samples are randomly selected and used as train set. The remaining samples are used as test set. The number of sample images for each class is different. Some classes are frequently found in our collection of palm leaf manuscripts, but some others are rarely used. Thumbnail samples of these images are showed in Fig. 9.

B. Experiments of character recognition

We investigated and evaluated the performance of 10 feature extraction methods and the proposed combination of features in 29 different schemes. We also compared the experimental result with the convolutional neural network. For all experiments, a set of image patches containing Balinese characters from the original manuscripts will be used as input, and a correct class of each character should be identified as a result. We used k=5 for k-NN classifier, and all images are resized to 50x50 pixels (the approximate average size of character in collection), except for Gradient features where images are resized to 81x81 pixels to get evenly 81 blocks of 9x9 pixels, as described in [START_REF] Fujisawa | Handwritten numeral recognition using gradient and curvature of gray scale image[END_REF].

The results (Table I) show that the recognition rate of NPW features can be significantly increased (up to 10%) by applying it on the four directional Kirsch edge images (NPW-Kirsch method). Then, by combining this NPW-Kirsch features, HoG features and Zoning method can increase the recognition rate up to 85%. This result is slightly better than 3 http://www.primaresearch.org/tools/Aletheia using the convolutional neural network. In our experiments, the number of training dataset for each classes is not balance, and it influences the performance of convolutional neural network. But this condition was already clearly stated and can not be avoided as real challenge of our IHCR development for Balinese script on palm leaf manuscripts. Some ancient characters are not frequently found in our collection of palm leaf manuscripts. V. CONCLUSIONS AND FUTURE WORKS Balinese scripts on palm leaf manuscripts offer a real new challenge in IHCR development. We present our experimental study on feature extraction methods for character recognition of Balinese script on palm leaf manuscripts by investigating 10 feature extraction methods for IHCR. We proposed the proper and robust combination of feature extraction methods to increase the recognition rate. Our study shows that the recognition rate can be significantly increased by applying NPW features on four directional Kirsch edge images. And the use of NPW on Kirsch features in combination with HoG features and Zoning method can increase the recognition rate up to 85%, and it still slightly better than using the convolutional neural network. For our future works, we will investigate more specific curve and curvature based features to achieve better recognition rate of Balinese script IHCR.

Fig. 1

 1 Fig. 1 Balinese script on palm leaf manuscripts

Fig. 3

 3 Fig. 3 Neighborhood pixels for NPW features

Fig. 6

 6 Fig. 6 Type of Zoning (from left to right: vertical, horizontal, block, diagonal, circular, and radial zoning)

Fig. 7

 7 Fig. 7 Scheme of NPW on Kirsch features

Fig. 8

 8 Fig. 8 Architecture of multilayer convolutional neural network IV. DATASET, EXPERIMENTS AND RESULTS

Fig. 9

 9 Fig. 9 Samples of character-level annotated patch images of Balinese script on palm leaf manuscripts

Table I .

 I Recognition rate from all schemes of experiment

	No	Method	Feature	Classifier	Recog.
			Dim.		Rate %
	1.	Histogram Projection (Binary)	100	SVM	26,.313
	2.	Celled Projection (Binary)	500	SVM	49,.9414
	3.	Celled Projection (Binary)	500	k-NN	76,.1632
	4.	Distance Profile (Binary)	200	SVM	40,.1277
	5.	Distance Profile (Binary)	200	k-NN	58,.947
	6.	Distance Profile (Skeleton)	200	SVM	36,.7653
	7.	Crossing (Binary)	100	SVM	15,.0007
	8.	Zoning (Binary)	205	SVM	50,.6451
	9.	Zoning (Binary)	205	k-NN	78,.5351
	10.	Zoning (Skeleton)	205	SVM	41,.848
	11.	Zoning (Grayscale)	205	SVM	52,.4176
	12.	Zoning (Grayscale)	205	k-NN	66,.128
	13.	Gradient Feature (Gray)	400	SVM	60,.0417
	14.	Gradient Feature (Gray)	400	k-NN	72,.5792
	15.	Moment Hu (Gray)	56	SVM	33,.481
	16.	Moment Hu (Gray)	56	k-NN	33,.481
	17.	HoG (Gray)	1984	SVM	71,.2759
	18.	HoG (Gray)	1984	k-NN	84,.3477
	19.	NPW (Binary)	100	SVM	51,.388
	20.	NPW (Gray)	100	SVM	54,.1249
	21.	Kirsch (Gray)	100	SVM	62,.4528
	22.	HoG with Zoning (Gray)	1984	SVM	69,.6859
	23.	HoG with Zoning (Gray)	1984	k-NN	83,.5006
	24.	NPW-Kirsch (Gray)	400	SVM	63,.5736
	25.	NPW-Kirsch (Gray)	400	k-NN	76,.7105
	26.	HoG on Kirsch edge (Gray)	1984*4	k-NN	82,.0931
	27.

http://www.vlfeat.org/api/hog.html

http://arxiv.org/abs/1603.04467

ACKNOWLEDGMENT

The authors would like to thank Museum Gedong Kertya, Museum Bali, and all families in Bali, Indonesia, for providing us the samples of palm leaf manuscripts, and the students from Department of Informatics Education and Department of Balinese Literature, Ganesha University of Education for helping us in ground truthing process for this experiment. This work is also supported by the DIKTI BPPLN Indonesian Scholarship Program and the STIC Asia Program implemented by the French Ministry of Foreign Affairs and International Development (MAEDI).