Maroua Mehri
email: maroua.mehri@univ-lr.fr

Van Cuong Kieu
email: vkieu@labri.fr

Mohamed Mhiri
email: mhirimohamed@hotmail.com

Pierre Héroux
email: pierre.heroux@univ-rouen.fr

Petra Gomez-Krämer

Mohamed Ali Mahjoub
email: medali.mahjoub@ipeim.rnu.tn

Rémy Mullot
email: remy.mullot@univ-lr.fr

Robustness Assessment of Texture Features for the Segmentation of Ancient Documents

Keywords: Ancient digitized document images, Texture, Multiresolution, Noise, Enhancement, Non-local means, Superpixel

For the segmentation of ancient digitized document images, it has been shown that texture feature analysis is a consistent choice for meeting the need to segment a page layout under significant and various degradations. In addition, it has been proven that the texture-based approaches work effectively without hypothesis on the document structure, neither on the document model nor the typographical parameters. Thus, by investigating the use of texture as a tool for automatically segmenting images, we propose to search homogeneous and similar content regions by analyzing texture features based on a multiresolution analysis. The preliminary results show the effectiveness of the texture features extracted from the autocorrelation function, the Grey Level Co-occurrence Matrix (GLCM), and the Gabor filters. In order to assess the robustness of the proposed texture-based approaches, images under numerous degradation models are generated and two image enhancement algorithms (non-local means filtering and superpixel techniques) are evaluated by several accuracy metrics. This study shows the robustness of texture feature extraction for segmentation in the case of noise and the uselessness of a denoising step.

I. INTRODUCTION

In the context of the DIGIDOC project (Document Image diGitisation with Interactive DescriptiOn Capability) 1 , we are interested in simplifying and improving the archiving, processing, comparison, and indexing of ancient digitized books collected from the Gallica digital library 2 . Indeed, various and numerous problems may arise for automated analysis of ancient document images. Their segmentation and/or characterization has to be sophisticated due to their particularities, such as noise and degradation, and the great variability of the page layout: complicated layout, random alignment, overlapping object boundaries, and the superimposition of information layers (stamps, handwritten notes, noise, back-to-front interference, etc.). Due to the nature and specifications of such documents (cf. Figure 2(a)), existing approaches (e.g. XY-CUT [START_REF] Khedekar | Text -Image Separation in Devanagari Documents[END_REF]) based on a priori knowledge, such as the repetitiveness of document structure in a corpus, are not effective. Thus, in the context of ancient document images, various aspects of the texture features are investigated in order to assist the analysis of the images by characterizing a document image layout by a hierarchy of homogeneous regions and to conceive a model of similarities concerning the book structures.

During the two last decades, several studies [START_REF] Journet | Document image characterization using a multiresolution analysis of the texture: application to old documents[END_REF], [START_REF] Mehri | Texture Feature Evaluation for Segmentation of Historical Document Images[END_REF] have characterized and indexed images of ancient documents by their content by exploring textural analysis. In our previous work [START_REF] Mehri | Texture Feature Evaluation for Segmentation of Historical Document Images[END_REF], a comparative study of three different well-known texture-based features (autocorrelation function, GLCM, and multiple channel Gabor filters), has been reported for segmentation of digitized ancient document images. The textural descriptors are extracted from only the foreground pixels of gray-level document images at four different sizes of sliding windows in order to adopt a multiscale approach. An adapted Hierarchical Ascendant Classification (HAC) [START_REF] Lance | A general theory of classificatory sorting strategies 1. Hierarchical systems[END_REF] is performed subsequently on the normalized textural features by setting the maximum number of homogeneous and similar content regions equal to the one defined in our ground truth. Texture features are described and their effectiveness is assessed by only clustering accuracy metrics on simplified ancient document images. We have shown that the best performance for several internal and external clustering accuracies is obtained by analyzing the Gabor primitives.

In this paper, based on [START_REF] Mehri | Texture Feature Evaluation for Segmentation of Historical Document Images[END_REF] a comparative study of robustness of texture descriptors (autocorrelation function, GLCM, and Gabor filters) for a pixel labeling system on semi-synthetic images is presented. The robustness is proven in the case of noise and degradation, and the uselessness of a denoising step is shown. This paper is organized as follows: Two preprocessing algorithms for enhancement/denoising are briefly described in Section II. The process of generating semisynthetic simplified ancient document images under numerous degradation models is presented in Section III. Finally, Section IV details the qualitative results and the quantitative performance of the extracted textural features. Our conclusions and future work are presented in Section V.

II. DOCUMENT ENHANCEMENT

The issue of degraded document image enhancement is well studied. For instance, Likforman-Sulem et al. [START_REF] Likforman-Sulem | Pre-Processing of Degraded Printed Documents by Non-local Means and Total Variation[END_REF] compare and combine two image pre-processing techniques (nonlocal means filtering [START_REF] Buades | A Non-Local Algorithm for Image Denoising[END_REF] and total variation [START_REF] Darbon | Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization[END_REF]) for enhancement/denoising degraded printed documents. In this section, two pre-processing algorithms are presented which are used afterwards in the robustness study. Thus, we assess a wellused non-local means filtering technique as a pre-processing step. Then, we propose to perform the superpixel technique as enhancement/denoising step.

A. Non-local means filtering technique

The non-local means filtering technique [START_REF] Buades | A Non-Local Algorithm for Image Denoising[END_REF] processes using a weighted average of the neighboring pixels of the analyzed pixel. The averaging weights are deduced from the similarities between a patch around the analyzed pixel and the neighboring patches located in a search window. Likforman-Sulem et al. [START_REF] Likforman-Sulem | Pre-Processing of Degraded Printed Documents by Non-local Means and Total Variation[END_REF] set the default parameters of the non-local means approach equal to 3 and 4 for patch and window sizes respectively for the enhancement of historical printed document images. By using the non-local means approach, the authors obtain an improvement of optical character recognition for a low level of degradation. As the non-local means has been proven to be efficient for historical document images, we adopt this approach here using the default parameters which are widely used in several applications. Figure 2(d) illustrates an example of enhanced image by the non-local means filtering technique and shows that the background is still noisy and there is no discernible difference to the naked eye with the original image (cf. Figure 2(c)).

B. Superpixel technique

The superpixel technique has recently been used in computer vision applications, such as segmentation [START_REF] Fulkerson | Class segmentation and object localization with superpixel neighborhoods[END_REF], Gaussian noise estimation [START_REF] Wu | Gaussian noise estimation with superpixel classification in digital images[END_REF], etc. Many superpixel algorithms have been proposed in the literature [START_REF] Achanta | SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[END_REF] since the superpixel approach becomes a consistent alternative of using a rigid structure of pixel grid, i.e. it is faster, more memory efficient, and more interesting to compute image features on each superpixel center than on each image pixel. Achanta et al. [START_REF] Achanta | SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[END_REF] categorize the existing superpixel methods into three classes: graph-based, gradient-ascent-based and Simple Linear Iterative Clustering (SLIC). Indeed, the superpixel approach gathers pixels sharing similar characteristics (texture cues, contour, color, etc.) into a significant polygon-shaped region. Thus, this superpixel approach produces an over-segmented image representing a compact content map [START_REF] Achanta | SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[END_REF].

Cohen et al. [START_REF] Cohen | Robust text and drawing segmentation algorithm for historical documents[END_REF] propose to separate drawings from background and noise of historical documents by using spatial and color features of superpixels. Liu et al. [START_REF] Liu | Entropy Rate Superpixel Segmentation[END_REF] propose an entropy rate superpixel segmentation algorithm based on a novel objective function. This objective function is based on entropy rate and balancing terms. The entropy rate ensures the classification of samples on homogeneous and compact clusters while the balancing term controls the size of clusters in order to obtain n superpixels with similar sizes. Firstly, the authors represent an over-segmented image by a weighted graph where each node corresponds to an image pixel, and each edge is weighted according to the similarity degree between the neighboring pixels. Then, the obtained graph is partitioned into n subgraphs maximizing the objective function using a greedy heuristic approach [START_REF] Nemhauser | An analysis of approximations for maximizing submodular set functions-I[END_REF]. The authors prove that their proposed graph-based algorithm performs considerably better with a smaller number of superpixels than the state-of-the-art superpixel methods [START_REF] Liu | Entropy Rate Superpixel Segmentation[END_REF].

In order to obtain enhanced backgrounds of noisy ancient documents, we have developed a novel foreground-background segmentation algorithm based on entropy rate superpixel segmentation [START_REF] Liu | Entropy Rate Superpixel Segmentation[END_REF] and k-means clustering [START_REF] Macqueen | Some Methods for Classification and Analysis of Multivariate Observations[END_REF] algorithms. An entropy rate superpixel segmentation algorithm is performed in order to reduce the step complexity of a foregroundbackground segmentation of ancient documents. By setting the number of superpixels n equal to 5% of image pixels, the entropy rate superpixel segmentation algorithm is carried out on each grayscale image. Therefore, over-segmented images are generated. Then, a mean gray-level value is assigned to each superpixel, which is deduced by averaging over all the gray-level pixels contained in the superpixel region. Then, the k-means clustering algorithm is performed on the computed mean gray-level values of superpixels without taking into account the spatial coordinates for each image by setting the number of clusters k equal to 2, in order to segment the image into two layers: the foreground and background. Since the clustering algorithm is performed, the background superpixels are only retrieved. Thereby, the values of graylevel background superpixels and their pixels of the original image will be changed after the clustering step by assigning the value of a white pixel (i.e. a 255 gray-level value) in order to obtain an enhanced and non-noisy background, i.e. the noisy pixels will have 255 gray-level values. The values of the graylevel foreground superpixels and their pixels of the original image are remained unchangeable. Figure 2(e) illustrates an example of enhanced image by the superpixel technique with a clean background.

III. DOCUMENT DEGRADATION MODEL

Basically, the different degradations are generated synthetically for noise estimation or the assessment of image enhancement or restoration algorithms [START_REF] Wu | Gaussian noise estimation with superpixel classification in digital images[END_REF]. Contrarily, in this paper, several degradations are added to images with the goal of evaluating the robustness of the extracted texture features on semi-synthetic degraded images. The degradation models used in this paper simulate noise and corruption effects.

Firstly, the linear degradation model is introduced by adding a mixture uniform and Gaussian noise on images (UGd). By adding to each pixel a random value from a zero-mean and 35-standard deviation Gaussian distribution, semi-synthetic degraded images (cf. Figure 2(l)) are produced by convolving the original image (cf. Figure 2(c)) with the generated mixture of additive uniform and Gaussian noise.

Secondly, six local noise regions defined by different degradation models (three noise kinds and three noise levels) are also added to images in order to generate semi-synthetic degraded images. Three kinds of local degradation models have been proposed in [START_REF] Kieu | An Efficient Parametrization of Character Degradation Model for Semisynthetic Image Generation[END_REF]: independent spots (Is), overlapping spots (Os), and disconnected spots (Ds). The Is noise is generated by adding dark/bright spots located inside/outside characters as in Figure 1(b). The Os noise is generated by adding dark/bright spots connected with the character edges as in Figure 1(c). Finally, Ds noise is generated when bright spots totally break the connectivity of characters as shown in Figure 1(d). To generate these local degradation models, seed-points (i.e. centre of noise regions) are selected from the binarized original image. Then, an elliptic-shaped noise region is defined around each seed-point according to the degradation model. Three other local degradation models: low (Ld), medium (Md), and high (Hd), are generated by combining the first three models (i.e. Is, Os, and Ds) after fixing the number of seed-points and the size of noisy regions. The last three local degradation models are defined for three different levels of noise. For each level, the proportion of each type of local degradation model is randomly chosen. Two sets for each of the six degradation models are generated with distinct parameters for each set (i.e. 12 sets of semi-synthetic degraded images are generated by adding local noise to images). The degradation model characteristics are presented in Table I. Finally, a mixture of Md2, uniform, and Gaussian (35standard deviation) noise (UGMd2) is added to images in order to evaluate the robustness of the proposed approach in the case of a mixture of linear and local degradation models noise.

IV. EVALUATION AND RESULTS

A. Experimental protocol

Since the task of real historical document image analysis and characterization (c.f Figure 2(a)) is tricky, a subset of simplified ancient document images was created, as a first stage of validation and analysis of the pros and cons of each texture feature (autocorrelation function, GLCM, and Gabor filter). By adding few occlusions in the document image (c.f Figure 2(a)), the real document layout is simplified and its complexity is reduced (c.f Figure 2 additive uniform and Gaussian noise. To assess the noise robustness of the extracted texture features on synthetically enhanced and corrupted document images, each image is segmented by analyzing the three texture primitives based on a multiresolution analysis. Extracting the three texture descriptors from the enhanced images gives a total of 150 analyzed images (50 enhanced images × 3 texture approaches). Then, performing the three texture-based approaches from the degraded images gives a total of 1050 analyzed images (350 degraded images × 3 texture approaches). The segmented results are compared to a specified groundtruth. Then, several clustering accuracy (silhouette width SW and purity per block PPB) and classification metrics (precision P, recall R, and classification accuracy CA) are computed for performance evaluation. The higher the values are, the better the results.

(a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (m) Fig.

B. Texture features

Figures 5(a), 5(f), and 5(k) illustrate qualitatively the effectiveness of the extracted texture features in discrimination of the foreground layers of an ancient simplified document image, particularly of two classes: text and graphics. By visual inspection of the segmented images, we note that the autocorrelation and GLCM approaches (c.f Figures 5(a) and 5(f)) provide satisfying results particularly in distinguishing the textual regions from the graphical ones. Nevertheless, the Gabor approach (c.f Figure 5(k)) confuses the uppercase text and the graphical components. This confusion can be explained by the limitations of the Gabor approach to separate close distinct kinds of information, i.e. the vertical spacing is too small. Indeed, the Gabor indices are extracted for a specified range of frequency and direction values. Thus, the performance of the Gabor approach depends directly on the layout document. However, by analyzing the Gabor primitives, we can see that distinct kinds of graphics can be discriminated in Figure 5(k). For the same document image, each cluster (identified by a given color) represents a similar or homogeneous region.

The results of the different accuracy measures for the set of 25 simplified ancient document images are presented in Figure 3 (cf. blue bar). The best performance is obtained using the Gabor approach for almost all evaluation metrics with 0.33%(SW ), 96%(PPB), 93%(P), 90%(R), and 93%(CA) without taking into consideration the topographical relationships of pixels. However, when the numerical complexity is taken into account, the Gabor approach is the highest resource-consuming (memory and computational time) approach. The time required to process a page (1965*2750 pixels) using the autocorrelation and the Gabor approaches is 2 and 6 minutes respectively while using the co-occurrence descriptors is reduced to only 14 seconds. The obtained values of the computed classification accuracy metrics are in concordance with the various internal and external clustering evaluation indices presented previously in [START_REF] Mehri | Texture Feature Evaluation for Segmentation of Historical Document Images[END_REF].

C. Document enhancement

In the first experiments, we compare qualitatively the performance of the segmented images using the three texture-based approaches detailed in [START_REF] Mehri | Texture Feature Evaluation for Segmentation of Historical Document Images[END_REF] (autocorrelation, GLCM, and Gabor), without adding a pre-processing step (cf. Figures 5(a Figures 5(c), 5(h), and 5(m)). By visual inspection of the segmented images, the obtained results for the three approaches by introducing a pre-processing step (the non-linear filtering and superpixel techniques) are relatively similar to those obtained without a pre-processing phase. Although, we note a slight deterioration for the Gabor approach (cf. Figure 5(m)) by introducing the superpixel algorithm as a pre-processing step and for the co-occurrence approach (cf. Figure 5(g)) by using the non-local means filtering technique. Contrarily, an improvement is obtained for the the GLCM approach (cf. Figure 5(h)) by using the superpixel technique. This improvement is due to the fact that the GLCM approach depends on the probability of occurrence of pixel pairs according to their gray levels and distance by considering the spatial relationship of pixels in the image. Then, by enhancing images, the foreground pixel pair properties change and the GLCM-based approach results improve. Figure 3 (cf. left column) illustrates the quantitative performance obtained on original and enhanced images by non-local means filtering and superpixel techniques. We note a slight gain in the average performance of the GLCM approach equal to 1%(SW ), 0%(PPB), 2%(P), 1%(R), and 3%(CA) when using the superpixel technique. Therefore, we confirm our hypothesis that this gain is due to the changes in pixel pair properties when using the superpixel technique as a pre-processing step. However, similar performance is obtained according to the most computed accuracy metrics when comparing the segmentation results on simplified and enhanced images. Therefore, we conclude that a denoising step in our approach can be neglected.

D. Document degradation models

In order to assess the noise robustness of the three texturebased approaches, degraded images are generated as detailed in section III. Firstly, each degradation model is evaluated in isolation. Secondly, we compare their respective performance. By visual inspection of the segmented degraded images (cf. Figures 5(d), 5(i), 5(n), 5(e), 5(j), and 5(i)), similar performance can be observed as the non-noisy image, i.e. the original images (cf. Figures 5(a), 5(f), and 5(k)). The result of the segmented degraded image represents the case when the clustering is performed by setting the number of types of content regions equal to 3 (i.e. the defined one on the ground-truth) +1 (i.e. the noisy foreground cluster has been taken into account). We can see that the texture feature can discriminate the noise cluster (i.e. the mixture of uniform and Gaussian noise) from the text/graphic clusters. In Figure 5(o), by degrading the image under a mixture of additive Md2, uniform, and Gaussian noise, the Gabor approach can to some extent distinguish different text fonts and graphics kinds. The clustering results on the noisy images (cf. Figure 5(o)) using the Gabor approach are better than the results obtained without noise (cf. Figure 5(k)). This can be justified by the fact that we specify a range of frequency and direction values specified a priori when extracting the Gabor indices and due to the random values of noise, the foreground pixels are correctly classified and the noise cluster is also identified. Fig. 4.

Evaluation of the extracted features on degraded images under different degradation models: silhouette width (SW ), purity per block (PPB), precision (P), recall (R), and classification accuracy (CA).

Figures 3 and4 show the quantitative performance obtained on original and degraded images under different types and levels of noise. We note that by adding a mixture of uniform and Gaussian noise and with taking in consideration the noisy cluster, similar performance is obtained. By comparing the quantitative performance obtained of the autocorrelation-based approach on original and degraded images under a mixture of uniform and Gaussian noise, we observe a slight drop in performance (SW and PPB metrics) and a gain in the average performance of P, R, and CA accuracies. This gain is due to the fact that the noise pixels are correctly classified, i.e. they are labeled as their neighboring pixels and our ground-truth is not-pixel-based, i.e. it is defined by spatial boundaries of regions with labels. This highlights the need for a pixel-based ground-truth. A slight drop in the average performance for the three texture-based approaches can be observed when adding the six levels of degradation models: Ld1, Ld2, Md1, Md2, Hd1, and Hd2. Some performance difference rates between the obtained results on simplified and degraded images are presented in Table II. On average, the three texture-based approach performance drops when adding noise at the high levels Hd1 and Hd2. The performance of the three features decreases slightly and progressively from the low level to the high level noise. We also observe a slight gain in GLCM-based approach performance of the average when adding the Is2 degradation model. This confirms our assumption that the noise pixels are correctly classified. By adding UGMd2 noise, the performance difference is relatively slight for the autocorrelation, GLCM, and Gabor approaches respectively. We conclude that the texture feature extraction for segmentation is robust in the case of different kinds and levels of noise since we note only a slight performance drop. This article studies the robustness of texture-based segmentation of ancient digitized document images in the case of noise. Three different texture-based features, autocorrelation function, GLCM, and multiple channel Gabor filters are assessed. Firstly, we conducted a comparative study of two image pre-processing algorithms: the non-local means filtering and superpixel techniques. Based on the computation of several clustering and classification accuracy metrics, we conclude the uselessness of a denoising step for three different texturebased segmentation approaches. In addition, another analysis is presented by evaluating the performance of texture feature classification on degraded images under numerous degradation models. Quantitative performance has also shown that the three texture-based features are robust to different kinds and levels of noise. The first aspect of future work will be to analyze the robustness of other texture descriptors such as the wavelets. Further work also needs to be done by combining various texture descriptors in order to construct an optimal texture feature set.

f) (g) (h) (i) (j) Gabor (k) (l) (m) (n) (o) (

  (a) Original (b) Is (c) Os (d) Ds Fig. 1. Examples of three types of noise in semi-synthetic documents: (a) a non-noisy image, (b) two independent dark/bright spots, (c) two overlapping dark/bright spots, and (d) disconnection bright spot.

  (b)). From four real ancient document images, containing graphics and text with different fonts (c.f Figure 2(a)), a subset of 25 simplified ancient document images (c.f Figure 2(b)) was generated. Then, from the 25 simplified ancient document images: • 50 enhanced images by the non-local means filtering and superpixel techniques • 350 degraded images are generated synthetically by adding different degradation models: • 25 degraded images under a mixture of additive uniform and Gaussian noise • 300 damaged images under local degradation models • 25 corrupted images using a mixture of Md2,

2 .

 2 Example of original, enhanced and degraded images: (a) a real ancient document image, (b) an simplified image, (c) a zoomed region of (b). Figures (d) and (e) show enhanced regions using non-local means filtering and superpixel techniques respectively. Figures (f), (g), (h), (i), (j), and (k) depict respectively degraded regions of (b) using aIs2, Os2, Ds2, Ld2, Md2, and Hd2 degradation models respectively. Figures (l) and (m) show a degraded region using a mixture of additive uniform and Gaussian noise and a mixture of Md2, additive uniform and Gaussian noise respectively.

  ), 5(f), and 5(k)), with the results by integrating the non-local means filtering technique (cf. Figures 5(b), 5(g), and 5(l)) and the superpixel technique (cf.

Fig. 3 .

 3 Fig.3. Evaluation of the extracted textural features using different accuracy metrics on enhanced images (non-local means filtering and superpixel techniques) and degraded images: silhouette width (SW ), purity per block (PPB), precision (P), recall (R), and classification accuracy (CA). The " * " and " * * " represent the case when the clustering is performed by setting the number of types of content regions equal to the defined one on the ground-truth, and to the defined one on the ground-truth +1 (i.e. the noisy foreground cluster has been taken into account) respectively.

Fig. 5 .

 5 Texture feature evaluation results on simplified ancient document images (cf. Figures 5(a), 5(f), and 5(k)), enhanced images by non-local means (cf. Figures 5(b), 5(g), and 5(l)) and superpixel (cf. Figures 5(c), 5(h), and 5(m)) techniques, and degraded images by adding a mixture of uniform and Gaussian noise (cf. Figures 5(d), 5(i), and 5(n)). Figures 5(e), 5(j), and 5(o) illustrate the clustering result obtained from degraded images by adding a mixture of Md2, uniform, and Gaussian noise). Because the process is unsupervised, the colors attributed to text or graphics may differ from one document image to another.

TABLE I .

 I LOCAL NOISE MODELS.

		Number of seed-points	Model/Size of noisy regions
	Is1	2000	Independent spots
	Is2	2500	Independent spots
	Os1	1500	Overlapping spots
	Os2	2000	Overlapping spots
	Ds1	1000	Disconnection spots
	Ds2	1500	Disconnection spots
	Ld1	1000	Low/5
	Ld2	1000	Low/8
	Md1	1500	Medium/7
	Md2	1500	Medium/9,5
	Hd1	2500	High/10
	Hd2	2500	High/12

TABLE II .

 II PERFORMANCE DIFFERENCE RATES.

							V. CONCLUSIONS AND FURTHER WORK
		Feature	SW	PPB	P	R	CA
	UGd	Gabor	1	-3	0	0	0
	UGd	Autocorrelation	-4	-4	6	6	6
	Hd1	Gabor	-4	-2	-18	-7	-8
	Hd2	Gabor	-4	-2	-13	-3	-5
	Is2	Co-occurrence	1	1	1	0	4
	UGMd2	Autocorrelation	-6	-9	-3	-3	-4
	UGMd2	Co-occurrence	-7	-4	-4	-5	1
	UGMd2	Gabor	0	-6	-6	-3	-3

The DIGIDOC project is referenced under ANR-10-CORD-0020.

http://gallica.bnf.fr


Maroua Mehri
email: maroua.mehri@univ-lr.fr

Mohamed Mhiri
email: mhirimohamed@hotmail.com

Pierre Héroux
email: pierre.heroux@univ-rouen.fr

Petra Gomez-Krämer

Mohamed Ali Mahjoub
email: medali.mahjoub@ipeim.rnu.tn

Rémy Mullot
email: remy.mullot@univ-lr.fr

Performance Evaluation and Benchmarking of Six Texture-based Feature Sets for Segmenting Historical Documents

Keywords: Historical digitized document images, Segmentation, Texture, Multiscale approach

Recently, texture-based features have been used for digitized historical document image segmentation. It has been proven that these methods work effectively with no a priori knowledge. Moreover, it has been shown that they are robust when they are applied on degraded documents under different noise levels and types. In this paper an approach of evaluating texture-based feature sets for segmenting historical documents is presented in order to compare them. We aim at determining which texture features could be more adequate for segmenting graphical regions from textual ones on the one hand and for discriminating text in a variety of situations of different fonts and scales on the other hand. For this purpose, six well-known and widely used texturebased feature sets (autocorrelation function, Grey Level Cooccurrence Matrix, Gabor filters, 3-level Haar wavelet transform, 3-level wavelet transform using 3-tap Daubechies filter and 3-level wavelet transform using 4-tap Daubechies filter) are evaluated and compared on a large corpus of historical documents. An additional insight into the computation time and complexity of each texture-based feature set is given. Qualitative and numerical experiments are also given to demonstrate each texture-based feature set performance.

I. INTRODUCTION

The development of the Internet and electronic publishing has increased the need for reliable and efficient document understanding and interpretation system. Moreover, the need to guarantee a lasting preservation and to provide a wider access to material, has generated a raising interest to document image analysis. Document image analysis has become a thriving topic of major interest of many researchers and one of the most explored fields in image analysis [START_REF] Nagy | Twenty years of document image analysis in PAMI[END_REF]. Besides, we are interested in historical digitized document image segmentation and characterization tools.

The literature shows that various problems may arise related to the particularities of historical documents, such as a great variability of the page layout such as noise and degradation, page skew, random alignment, specific fonts, presence of embellishments, variations in spacing between the characters, words, lines, paragraphs and margins, overlapping object boundaries and the superimposition of information layers (stamps, handwritten notes, noise, back-to-front interference, etc.) [START_REF] Agam | Degraded document image enhancement[END_REF]. Thus, the processing of such kind of document is a non-trivial task, since we need to segment a page layout under significant and various degradations and without hypothesis on the document structure (document model, typographical parameters, etc.). It has been documented in the literature that texture feature analysis is fundamental to numerous applications including artificial vision, automated inspection, medical image segmentation, urban mapping, satellite image analysis and remote sensing. Furthermore, the use of texture analysis techniques has proliferated over the past two decades such that it has become the appropriate choice for the analysis of many types of images, particularly on synthetic, medical and natural images. More recently, these approaches have been investigated for document processing, such as script and language identification [START_REF] Busch | Texture for script identification[END_REF].

Our goal is to identify homogenous regions or similar groups of pixels sharing some visual characteristics by labeling and grouping pixels from historical document images. Thus, we explore and compare the various aspects of the texture features by using a texture-based segmentation scheme in order to assist the document image analysis. For our research, nontext regions and text regions are expected to have different textural characteristics. Moreover, different text fonts have distinctive visual appearance, so different texture properties [START_REF] Julesz | Visual pattern discrimination[END_REF]. The texture-based methods have proven adequate for meeting the need to segment an image under significant and various degradations and with little a priori knowledge [START_REF] Journet | Document image characterization using a multiresolution analysis of the texture: application to old documents[END_REF], [START_REF] Mehri | Texture feature evaluation for segmentation of historical document images[END_REF]. In our previous work [START_REF] Mehri | Texture feature evaluation for segmentation of historical document images[END_REF], three well-known texture features, extracted from the autocorrelation function [START_REF] Petrou | Image processing: dealing with texture[END_REF], the Grey Level Co-occurrence Matrix (GLCM) [START_REF] Haralick | Textural features for image classification[END_REF] and Gabor filters [START_REF] Gabor | Theory of communication. Part 1: The analysis of information[END_REF], are detailed and evaluated on 25 simplified historical document images. In this research, we extend our work by integrating three wavelet-based descriptors [START_REF] Mallat | A theory for multiresolution signal decomposition: The wavelet representation[END_REF]: 3-level wavelet transform using Haar filter (Haar), 3-level wavelet transform using 3tap Daubechies filter (Daubechies3) and 3-level wavelet transform using 4-tap Daubechies filter (Daubechies4), which are assessed on more than 300 historical documents.

The paper is summarized as follows. First, Section II presents a brief review of the different texture-based feature sets and particularly the new investigated texture-based feature sets in this work, i.e. wavelet descriptors. Next, an overview of the control scheme of texture-based segmentation is outlined in Section III. Then, the experimental protocol and the results of texture feature analysis experiments are detailed in Sections IV and V respectively. Finally, conclusions from this research and future plans are presented in Section VI.

II. TEXTURE FEATURES

Numerous feature extraction algorithms have been proposed. Texture feature analysis methods may be divided into four categories [START_REF] Chen | Texture analysis in the handbook of pattern recognition and computer vision[END_REF]:

• Statistical methods analyze the spatial distribution of gray level values by computing local indices in the image and deriving a set of statistics from the distributions of the local features. The autocorrelation function [START_REF] Petrou | Image processing: dealing with texture[END_REF] and the GLCM [START_REF] Haralick | Textural features for image classification[END_REF] are the classics of the statistical texture-based segmentation methods.

• Geometrical methods are used to describe intricate patterns, to retrieve and to describe texture primitives.

The texture primitives may be extracted for example by the difference-of-Gaussian filter [START_REF] Tuceryan | Texture segmentation using Voronoi polygons[END_REF]. Those methods attempt to characterize the primitives and find the rules governing their spatial organization.

• Model-based methods compute a parametric generative model based on the intensity distribution of the texture primitives. A widely used class of the model-based methods are probabilistic models, e.g. Conditional Random Fields (CRF) [START_REF] Lafferty | Conditional Random Fields: probabilistic models for segmenting and labeling sequence data[END_REF].

• Frequency methods investigate the global frequency content of the analyzed image. The most widely used frequency methods are Gabor filters [START_REF] Gabor | Theory of communication. Part 1: The analysis of information[END_REF], wavelet transforms [START_REF] Mallat | A theory for multiresolution signal decomposition: The wavelet representation[END_REF], etc. Some approaches investigate the local properties of the analyzed image. Other methods are based on statistical and/or spatial and/or frequency representations.

A well-researched survey and complete overview of recent texture segmentation and feature extraction techniques is presented in [START_REF] Reed | A review of recent texture segmentation and feature extraction techniques[END_REF], including Gabor filters, GLCM, fractals, etc. Qiao et al. [START_REF] Qiao | Document image segmentation using Gabor wavelet and kernel-based methods[END_REF] combine Gabor, wavelets and kernelbased methods for document image segmentation. Nourbakhsh et al. [START_REF] Nourbakhsh | Text localization and extraction from complex gray images[END_REF] evaluate two texture-based feature sets: Gabor filters and log-polar wavelets for separating text/non-text in a document image. In this paper, six types of texture primitives are computed for segmentation and characterization of historical digitized document images: autocorrelation, co-occurrence, Gabor and three wavelets features (Haar, Daubechies3 and Daubechies4).

In our work, we choose to analyze the six texture-based feature sets, basically statistical and frequency methods for several reasons: Firstly, we have made a comparative study about choosing the texture feature category, which ensures the best and constructive trade-off between best performances, reduced number of parameter settings and thresholds and lowest computation time [START_REF] Mehri | Texture feature evaluation for segmentation of historical document images[END_REF]. Secondly, the extraction of these texture features needs less parameter settings. Indeed, without hypothesis on the document structure, neither on the document model (layout) nor the typographical parameters, the choice of numerous appropriate thresholds and parameters is a very difficult task. Then, the pertinence of the segmentation experiments based on autocorrelation function [START_REF] Journet | Document image characterization using a multiresolution analysis of the texture: application to old documents[END_REF], GLCM [START_REF] Busch | Texture for script identification[END_REF], multiple channel Gabor filters [START_REF] Jain | Unsupervised texture segmentation using gabor filters[END_REF] and wavelets [START_REF] Kumar | Text extraction and document image segmentation using matched wavelets and MRF model[END_REF] leads us to evaluate and compare these six textural features for segmenting historical documents. Finally, texture descriptors are basically investigated in independent experiments for document segmentation. Based to our knowledge, there are no comparative studies of texture features for historical document segmentation. Therefore, this study evaluates and compares some of the well-known and widely used texture-based feature sets, including autocorrelation function, GLCM, multiple channel Gabor filters and three different wavelet transforms, used for segmentation of digitized historical documents. All our experiments are performed with no a priori knowledge about the layout document nor its content. Autocorrelation, GLCM and Gabor features are detailed in [START_REF] Mehri | Texture feature evaluation for segmentation of historical document images[END_REF]. In this research, we extend our work to study and evaluate wavelet features and compute the six texture-based feature sets on more than 300 historical documents.

The new investigated features in our work are wavelet descriptors. Even if the wavelet transform is computationally expensive (i.e. it is carried out by a large combination of filter parameters), it has been proven to be a promising alternative of many texture approaches such as Gabor filters [START_REF] Liu | Automatic texture segmentation for texture-based image retrieval[END_REF]. For page segmentation, Gupta et al. [START_REF] Gupta | Wavelet based page segmentation[END_REF] study the distribution of energy over different scales of wavelet decomposition. For document image segmentation, Li and Gray [START_REF] Li | Context-based multiscale classification of document images using wavelet coefficient distributions[END_REF] use distribution characteristics of wavelet coefficients of the 1-level Haar, 4-tap Daubechies and 8-tap Daubechies wavelet transforms. Kumar et al. [START_REF] Kumar | Locating text in images using matched wavelets[END_REF] compare Haar discrete wavelet transforms and match wavelets for text extraction and document image segmentation. Acharyya and Kundu [START_REF] Acharyya | Document image segmentation using wavelet scale-space features[END_REF] present a multiscale analysis method based on wavelet scale-space features using an 8-tap filter for text segmentation in document images. In [START_REF] Etemad | Multiscale segmentation of unstructured document pages using soft decision integration[END_REF], Etemad et al. present an algorithm based on a pyramidal wavelet transform and a wavelet packet tree using Daubechies filters for segmentation of unstructured document pages.

Haar and Daubechies wavelets are the most used ones since it has been proven that they work effectively in many applications. The Haar wavelet transform is the fastest among all wavelets since its coefficients are either 1 or -1. Thus, they are the less complex, simplest and most widely used wavelets, while Daubechies ones are characterized by fractal structures [START_REF] Liang | DWT based text localization[END_REF], [START_REF] Xavier | Content based image retrieval using textural features based on pyramid-structure wavelet transform[END_REF]. The 1st-order and 2nd-order statistics of coefficient sub-bands, such as the mean and standard deviation of wavelet coefficients, are the most commonly used features for texture classification and segmentation problems. Myint et al. [START_REF] Myint | Wavelets for urban spatial feature discrimination: comparisons with fractal, spatial autocorrelation, and spatial co-occurrence approaches[END_REF] prove that classification accuracy decreases when the wavelet decomposition level is high for urban spatial feature discrimination. In [START_REF] Angadi | A fuzzy approach for word level script identification of text in low resolution display board images using wavelet features[END_REF], Angadi and Kodabagi report that wavelet coefficients are the most suitable for texture representation.

The objective of wavelet transform based methods is to decompose an image into low-frequency and high-frequency sub-band images, i.e. to filter out several frequency ranges. The wavelet transform represents an image with both spatial and frequency characteristics. The two-dimensional J-level wavelets transform decomposes a discrete input image I(x, y) into four sub-bands and it produces 3J + 1 sub-images [START_REF] Mallat | A theory for multiresolution signal decomposition: The wavelet representation[END_REF]:

A 2 -J I, {D (1) 2 -j , D (2) 2 -j , D (3) 2 -j } j=1,2,...,J (1)
where A 2 -J is the approximation of the input image I(x, y) at resolution 2 -J ; D

(1)

2 -j , D (2)
2 -j and D

(3) 2 -j are 3 detail components of the input image I(x, y) at resolution 2 -j . Wavelet coefficients in D

(1)

2 -j , D (2)

-j and D

(3) 2 -j represent respectively vertical, horizontal and diagonal high frequencies.

By applying a 3-level wavelet transform to each analyzed block image, 10 sub-bands are produced (A 2 -3 , D

(1)

2 -1 , D (2) 2 -1 , D (3) 2 -1 , D (1) 2 -2 , D (2) 2 -2 , D (3) 2 -2 , D (1) 2 -3 , D (2)

-3 and D

(3) 2 -3 ). In our experiments, the mean and standard deviation of each of the transform coefficients, i.e. 10 sub-band coefficients are extracted to form a feature vector of 20 terms. The wavelet features are illustrated in Table I, where C(i, j) is the transform coefficient, and S w and S h are the width and the height respectively of a sub-band in the wavelet domain.

Feature Expression

Mean

F (1) = ∑ Sw i=0 ∑ S h j=1 C(i, j) S w * S h Standard deviation F (2) = ∑ Sw i=0 ∑ S h j=1 [C(i, j)-F (1) ] 2 S w * S h

III. TEXTURE-BASED SEGMENTATION SCHEME

In order to evaluate and compare the six texture-based feature sets, a texture-based segmentation scheme is proposed. Our texture-based segmentation scheme has been conceptualized as four modular processes: pre-processing, texture-feature extraction, segmentation of homogeneous regions based on the feature values by using a pixel clustering technique and a relaxation process (cf. Figure 1). Textural descriptors are only extracted from the selected foreground pixels of the gray-level document images at four different sizes of sliding windows in order to adopt a multiscale approach. The foreground pixel selection step is performed by using a standard non-parametric binarization method, the Otsu method, for the purpose of retrieving only pixels representing the information of the foreground (noise, text fields, graphics, etc.). In order to avoid side effects, we use classically border replication allowing computing texture features on the whole image. Then, a clustering algorithm is performed on the normalized textural features by setting the maximum number of homogeneous and similar content regions equal to the ones defined in our ground-truth [START_REF] Mehri | Texture feature evaluation for segmentation of historical document images[END_REF]. II). Two conventional clustering algorithms, k-means and Hierarchical Ascendant Classification (HAC), have been assessed for segmentation of homogeneous regions based on the extracted texture-based feature values [START_REF] Mehri | Texture feature evaluation for segmentation of historical document images[END_REF]. The pixel clustering task is performed by partitioning texture-based feature sets into compact and well-separated clusters in the feature space. It does not include spatial information. In this work, we add the multiscale majority voting technique as a relaxation process by taking into consideration the topographical relationships of pixels.

For refinement of the segmentation results, many researchers have introduced the relaxation labeling process as a post-processing step. Based on Chang and Kuo [START_REF] Chang | Texture segmentation with tree-structured wavelet transform[END_REF] the use of a relaxation labeling process is justified by the two following reasons. Firstly, by using a relaxation labeling process, we integrate the topographical or spatial relationships between pixels (median filtering or morphological dilations) which have not been considered when the texture features were extracted and analyzed. Secondly, the authors confirm that texture feature extraction from small predefined windows is not a relevant choice since this technique can generate nonsmoothed boundaries. Thus, the segmentation performance will be improved and the variation produced by the predefined size of analysis window can be reduced. Etemad et al. [START_REF] Etemad | Page segmentation using decision integration and wavelet packets[END_REF] use weighted majority votes in the decision integration scheme in order to identify text, images and graphic areas on a document page after exploiting the wavelet packets technique. For document image segmentation, Jain et al. [START_REF] Jain | On texture in document images[END_REF] introduce a connected component analysis in order to obtain smoothed text rectangular blocks after extracting Gabor features. For multiscale segmentation of unstructured document pages, the authors in [START_REF] Etemad | Multiscale segmentation of unstructured document pages using soft decision integration[END_REF] integrate morphological operations (closing operations) applying on the image regions after analyzing wavelet-based features in order to eliminate noise or outliers from segmented regions.

In our work, the spatial or topographical relationship between pixels is introduced by integrating a spatial multiscale analysis of majority votes since it is parameter-free and it is widely used in case of historical document segmentation. A multiscale majority voting technique is used to remove small isolated groups of pixels. By performing a multiscale approach in the majority voting technique, a local decision on each selected pixel is taken using the maximum number or majority of pixel labels performed at four different sizes of sliding windows.

IV. EXPERIMENTAL PROTOCOL

In order to give insight about the robustness assessment of texture features for historical document segmentation, we aim to assess the discriminant power of texture features and determine which clustering algorithm could be better suited, by testing two conventional clustering algorithms: k-means and HAC. Secondly, we present the experimental evaluation of each feature set. Finally, the contribution of a relaxation labeling task is analyzed in order to point out the role of the spatial or topographical relationship between pixels.

In the context of the DIGIDOC project (Document Image diGitisation with Interactive DescriptiOn Capability) 1 , we are interested in simplifying and improving the archiving, processing, comparison and indexing of ancient digitized books collected from the Gallica digital library (http://gallica.bnf.fr). Thus, in our experiments 314 pages are collected from Gallica, encompassing six centuries (1200-1900) of French history. Our experimental corpus is composed of:

• 89 pages containing only two fonts • 60 pages containing only three fonts • 108 pages containing graphics and one text font • 57 pages containing graphics and text with two different fonts To evaluate quantitatively the different obtained results, the following clustering and classification accuracy measures are computed: silhouette width (SW ), purity per block (PPB), F-measure (F) and classification accuracy (CA). The higher the values, the better are the results. In Table III, there are two "Overall" values. The "Overall * " value is obtained by averaging all the respective column values except the value of "Two fonts and graphics * * ". The "Overall * * " value is obtained by averaging all the respective column values except the value of "Two fonts and graphics * ". The "Two fonts and graphics * " value represents the case when every font in the text has a distinct label in the ground-truth and the clustering is performed by setting the number of types of content regions equal to 3 (graphics and text with two different fonts). The "Two fonts and graphics * * " value represents the case when all fonts in the text have the same label in the ground-truth and the clustering is performed by setting the number of types of content regions equal to 2 (graphics and text). This distribution points out which texture features could be more adequate for segmenting documents containing two text fonts and graphics into two/three classes, i.e. separating two distinct text fonts when the documents contain graphics.

Analyzing the six sets of texture descriptors from our corpus by one clustering algorithm with and without integrating a post-processing task gives a total of 4452 analyzed document images (314 + 57 images × 6 texture approaches × 2). The experiment was run on a SGI Altix ICE 8200 cluster (1 CPU and 2 gigabytes (GB) allocated memory on a Quad-Core X5355@2.66GHz running Linux).

V. EVALUATION AND RESULTS

The first set of experiments is performed by using two different algorithms (k-means and HAC) in order to compare their performance and to determine which one is more appropriate. For k-means, when using "Two fonts and graphics * * " in computing "Overall * * ", we observe that the classification performance of the extracted autocorrelation descriptors are much better, i.e. overall performance gains of 7%(SW ), 3%(PPB), 4%(F) and 2%(CA) are noted. Similarly for HAC, we observe overall performance gains of 8%(SW ), 1%(PPB), 5%(F) and 2%(CA). We subsequently show that autocorrelation attributes are more suitable to separate text from non-text regions. 1 The DIGIDOC project is referenced under ANR-10-CORD-0020.

By comparing the two clustering methods, k-means and HAC, higher performances are obtained by using HAC, i.e. the following overall performance gains of 4% and 2%(PPB), 2% and 3%(F) and 4% and 4%(CA) are observed respectively for "Overall * " and "Overall * * ". However, we note a slight drop in performance of the average of silhouette width of -2% and -1% respectively for "Overall * " and "Overall * * ". This can be explained by the progressive merge process of HAC, where in higher levels in the hierarchy, two distant data points can be merged together and yet still belong to the same cluster after cutting the dendrogram. This causes a slightly lower value of the silhouette width. This justification can be strengthened by the particularity of the average silhouette width as internal or unsupervised accuracy clustering evaluation which investigates the coherence of a clustering solution by measuring how observations are close to the cluster center and how clusters are well-separated. In conclusion, most of the computed accuracy evaluation metrics are in agreement that the HAC performs better than k-means.

The second set of experiments aims at determining which texture-based method is better suited for discriminating graphical regions from textual ones on the one hand and for separating textual regions with different sizes and fonts on the other hand, by comparing the six computed categories of texture features. Moreover, an additional insight into the computation time and complexity is provided in Table II. The processing time depends highly on the resolution, size of the input image and number of foreground pixels. An example of a full page document scanned at 300 dpi (1965*2750 pixels) is illustrated in Table II. The highest time required to process a page (1965*2750 pixels) is obtained when using wavelet approaches while the lowest one is obtained when using co-occurrence descriptors, i.e. it is reduced to only 14 seconds. The computation times of each texture feature sets are in concordance with the complexity. We can see that the Daubechies4-based approach has the highest complexity while the lowest one is noted for the co-occurrence-based approach (cf. Table II). Therefore, this study states the co-occurrencebased approach as the best one in terms of processing time and complexity. However, the co-occurrence and Gabor-based approaches are the highest memory-consuming. Nevertheless, we note the increase of the feature dimension of the Gaborbased approach, i.e. the Gabor signature corresponds to a set of vectors composed of 192 numeric values.

A comparison of the results generated by using the six texture-based feature sets in our texture-based segmentation scheme has been demonstrated in Table III. The computed clustering and classification accuracy values are congruent and very promising. Here, it can be observed that the result of employing Gabor features (cf. Figure 2(a)) yields a better output than the five other extracted textural features for almost all evaluation accuracy metrics without taking into consideration the topographical relationships of pixels. We also observe that the wavelet-based approaches and more specifically Daubechies3 and Daubechies4 (cf. Figure 2(e)), perform slightly superior to the Gabor one in the cases of "One font and graphics" and "Two fonts and graphics * * ", i.e. the waveletbased approach is more suitable for distinguishing textual regions from graphical ones. However, when the numerical complexity is taken into account, the wavelet approach is the highest resource-consuming (computation time) approach. We Complexity

O(M(θaW log 2 W )) O(Mdc G 2 ) O( fg θg (N 2 log 2 N)) O(M(4JW 2 log 2 W )) O(M(6JW 2 log 2 W )) O(M(8JW 2 log 2 W ))
Size of the texture vector

20 = I a ×W 72 = I c ×W 192 = I g ×W 80 = I h ×W 80 = I db3 ×W 80 = I db4 ×W
Number of the texture attributes

I a = 5 I c = 8d c + 2 = 18 I g = 2 f g θ g = 48 I h = 2I A 2 -J + 2I D (1) 2 -1
+ • • • + 2I D (1) 2 -j + • • • + 2I D (1) 2 -J + 2I D (2) 2 -1 + • • • + 2I D (2) 2 -j + • • • + 2I D (2) 2 -J + 2I D (3) 2 -1
+ • • • + 2I D (3) 2 -j + • • • + 2I D (3) 2 -J = 20
I db3 = 2I A 2 -J +2I D (1) 2 -1
+ • • • + 2I D (1) 2 -j + • • • + 2I D (1) 2 -J + 2I D (2) 2 -1 + • • • + 2I D (2) 2 -j + • • • + 2I D (2) 2 -J + 2I D (3) 2 -1
+ • • • + 2I D (3) 2 -j + • • • + 2I D (3) 2 -J = 20
I db4 = 2I A 2 -J +2I D (1) 2 -1
+ • • • + 2I D (1) 2 -j + • • • + 2I D (1) 2 -J + 2I D (2) 2 -1 + • • • + 2I D (2) 2 -j + • • • + 2I D (2) 2 -J + 2I D (3) 2 -1 + • • • + 2I D (3) 2 -j + • • • + 2I D (3) 2 -J = 20
where I a , I c , I g , I h , I db3 and I db4 are autocorrelation, co-occurrence, Gabor, Haar, Daubechies3 and Daubechies4 attributes respectively. A † , B ‡ and C represent the evaluation of the analysis of the extracted texture features in the case of without a post-processing task, with a post-processing step and the difference between A † and B ‡ respectively. Table cells whose background are yellow and green note the best performance of the analysis of the extracted texture features in the case of without a post-processing task (A † ) and with a post-processing step (B ‡ ) respectively. note that the Gabor approach performs considerably better in segmenting documents containing only textual regions with distinct fonts (cf. Figures 2(c) and 2(g)) (18%(F) performance difference rate between the co-occurrence and Gabor approaches). We observe that the lowest values of the most computed clustering and classification accuracy metrics are obtained for documents containing only distinct fonts when using co-occurrence descriptors. Therefore, we conclude that co-occurrence features are not adequate for separating different text fonts even when it is the lowest time-consuming.

I A 2 -J , I D (1) 2 -j , I D (2
† B ‡ C A † B ‡ C A † B ‡ C A † B ‡ C A † B ‡ C A † B ‡ C
By considering the multiscale approach of majority votes as a relaxation labeling process or a post-processing step, we aim at refining the segmentation results by integrating the topographical relationships of pixels. It is important to point out that our texture-based segmentation scheme is independent of the post-processing task, since the overall results are reasonably promising, i.e. we obtain 80% and 86% of overall F-measure rates for "Overall * " and "Overall * * " respectively without taking into consideration the topographical relationships of pixels and their labels. Nevertheless, we note that the Gabor and Daubechies4-based approaches provide the best performance when adding the relaxation labeling process in our texture-based segmentation approach. Furthermore, we note that Gabor feature analysis does not require a postprocessing phase, since there is a no performance difference between the cases of without and a post-processing task (cf. Figures 2(b), 2(f), 2(d) and 2(h)). It is worthwhile to note that by adding the multiscale analysis of majority voting as a postprocessing phase, the Daubechies4-based approach achieves approximately the performance of the Gabor-based approach (86% of mean F). Although, when the numerical complexity is taken into account by comparing the Gabor and Daubechies4 approaches, the Gabor one would be the better choice, since it also does not require a relaxation labeling process.

VI. CONCLUSIONS AND FURTHER WORK

This study presents an approach for evaluating and benchmarking six texture-based feature sets, including autocorrelation, co-occurrence and three kinds of wavelet transformbased features. This work has shown the effectiveness of texture analysis approaches in the field of historical document segmentation. Based on our experiments, we can conclude that Gabor and Daubechies4 features are the best choices for discriminating textual regions from graphical ones when taking into account the spatial relationships between pixels, i.e. by introducing a relaxation labeling step. However, when the numerical complexity is taken into account and the uselessness of a relaxation process, the Gabor approach would be the better choice. Furthermore, the Gabor approach is a good choice for segmenting document images containing only textual regions with distinct fonts.

Further work needs to be done in combining the extracted texture descriptors in order to construct an optimal texturebased feature set and to provide a qualitative measure of which features are the most appropriate for segmenting historical documents.

Fig. 1 .

 1 Fig. 1. Proposed texture-based segmentation scheme.

  Extracting the texture feature indices for autocorrelation, co-occurrence, Gabor, Haar, Daubechies3 and Daubechies4 using four different sliding window sizes gives a total of 20 (5 autocorrelation indices × 4 sliding window sizes for multiresolution), 72 (18 cooccurrence indices × 4 sliding window sizes), 192 (48 Gabor indices × 4 sliding window sizes), 80 (20 Haar indices × 4 sliding window sizes), 80 (20 Daubechies3 indices × 4 sliding window sizes) and 80 (20 Daubechies4 indices × 4 sliding window sizes) features respectively (cf. Table

Fig. 2 .

 2 Fig. 2. Examples of segmentation results: 2(a), 2(b), 2(e) and 2(f) represent the "Two fonts and graphics * * " category of documents. 2(c), 2(d) , 2(g) and 2(h) represent the "Only three fonts" category of documents. Because the process is unsupervised, the colors attributed to text or graphics may differ from one document image to another.

TABLE I

 I

	.	WAVELET FEATURES EXTRACTED FROM THE WAVELET
		TRANSFORM.

TABLE II .

 II COMPUTATION TIME AND COMPLEXITY: AN EXAMPLE OF HISTORICAL DOCUMENT IMAGE (1965*2750 PIXELS).

		Autocorrelation	Co-occurrence	Gabor	Haar	Daubechies3	Daubechies4
	Running time	00:02:33	00:00:14	00:06:05	00:29:17	00:37:53	00:42:21
	Used memory	≈48MB	≈587MB	≈552MB	≈61MB	≈61MB	≈63MB

  W is the number of sliding window sizes equal to 4. M is the number of foreground pixels. N is the dimension of the input N × N image. G is the number of gray levels, i.e. 255 gray levels. θ a is the number of orientation values of the rose of directions, i.e. 180 orientation values. d c is the GLCM particular distance defined in the probability of the gray level pairs. In our work, d c is equal to 2. f g and θ g are spatial frequency and orientation of Gabor filters respectively. In the experiment, the scale of wavelet decomposition J was 3 levels, i.e. from first, second and third scale. "MB" means megabytes. The time duration has "hh:mm:ss" format[START_REF] Mehri | Texture feature evaluation for segmentation of historical document images[END_REF].

	and detail sub-images attributes.	) 2 -j	and I D (3) 2 -j	are the approximation

TABLE III .

 III EVALUATION OF THE ANALYZED TEXTURAL FEATURES AND THE RELAXATION LABELING PROCESS.

	Autocorrelation	Co-occurrence	Gabor	Haar	Daubechies3	Daubechies4
	A