J. André and M. A. Chabin, Les documents anciens, 1999.

F. Lebourgeois, E. Trinh, B. Allier, V. Eglin, and H. Emptoz, Document images analysis solutions for digital libraries, International Workshop on Document Image Analysis for Libraries, pp.2-24, 2004.

F. Lebourgeois and H. Emptoz, DEBORA: Digital AccEss to BOoks of the RenAissance, International Journal of Document Analysis and Recognition, pp.193-221, 2007.

M. Baechler, A. Fischer, N. Naji, R. Ingold, H. Bunke et al., HisDoc: Historical document analysis, recognition, and retrieval, Digital Humanities -International Conference of the Alliance of Digital Humanities Organizations (ADHO), 2012.

J. M. Ogier and K. Tombre, Madonne: Document image analysis techniques for cultural heritage documents, International Conference on Digital Cultural Heritage, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00078300

T. M. Rath and R. Manmatha, Word spotting for historical documents, International Journal of Document Analysis and Recognition, pp.139-152, 2007.

H. S. Baird, Digital libraries and document image analysis, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., pp.2-14, 2003.
DOI : 10.1109/ICDAR.2003.1227619

J. M. Ogier, Ancient document analysis: A set of new research problems, Colloque International Francophone sur l'Ecrit et le Document, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00335043

M. Coustaty, R. Raveaux, and J. M. Ogier, Historical document analysis: A review of French projects and open issues, European Signal Processing Conference. EURASIP, pp.1445-1449, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01247944

O. Okun and M. Pietikäinen, A SURVEY OF TEXTURE-BASED METHODS FOR DOCUMENT LAYOUT ANALYSIS, Workshop on Texture Analysis in Machine Vision, pp.137-148, 1999.
DOI : 10.1142/9789812792495_0012

A. Piper, Reading's refrain: From bibliography to topology Readings: Selected Essays from the English Institute, pp.373-399, 2013.

E. T. Nalisnick and H. S. Baird, Extracting Sentiment Networks from Shakespeare's Plays, 2013 12th International Conference on Document Analysis and Recognition, pp.758-762, 2013.
DOI : 10.1109/ICDAR.2013.155

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.638.3305

G. Agam, G. Bal, G. Frieder, and O. Frieder, Degraded document image enhancement, Document Recognition and Retrieval. SPIE, 2007.
DOI : 10.1117/2.1200704.0681

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.146.753

L. Likforman-sulem, Apport du traitement des imagesàimages`imagesà la numérisation des documents anciens, pp.13-26, 2003.
DOI : 10.3166/dn.7.3-4.13-26

URL : http://www.cairn.info/load_pdf.php?ID_ARTICLE=DN_073_0013

J. André, H. Richy, L. Likforman-sulem, and G. Ventabert, Electronic representation and use of old documents (texts and images): About Philectre project experiments, Document Numérique, pp.57-73, 1999.

L. Likforman-sulem, A. Zahour, and B. Taconet, Text line segmentation of historical documents: a survey, International Journal of Document Analysis and Recognition (IJDAR), vol.26, issue.6, pp.123-138, 2007.
DOI : 10.1007/s10032-006-0023-z

G. Nagy and S. Seth, Hierarchical representation of optically scanned documents, International Conference on Pattern Recognition, pp.347-349, 1984.

F. M. Wahl, K. Y. Wong, and R. G. Casey, Block segmentation and text extraction in mixed text/image documents, Computer Graphics and Image Processing, pp.375-390, 1982.

Y. P. Zhou and C. L. Tan, Hough technique for bar charts detection and recognition in document images, International Conference on Image Processing, pp.605-608, 2000.

A. Bela¨?dbela¨?d and N. Ouwayed, Guide to OCR for Arabic scripts: Segmentation of ancient Arabic documents, 2011.

N. Nikolaou, M. Makridis, B. Gatos, N. Stamatopoulos, and N. Papamarkos, Segmentation of historical machine-printed documents using Adaptive Run Length Smoothing and skeleton segmentation paths, Image and Vision Computing, vol.28, issue.4, pp.590-604, 2010.
DOI : 10.1016/j.imavis.2009.09.013

J. Serra, Image analysis and mathematical morphology, 1982.

I. Granado, M. Mengucci, and F. Muge, Extraction de textes et de figures dans les livres anciensàanciens`anciensà l'aide de la morphologie mathématique, Colloque International Francophone sur l'Ecrit et le Document, 2000.

F. Muge, I. Granado, M. Mengucci, P. Pina, V. Ramos et al., Automatic Feature Extraction and Recognition for Digital Access of Books of the Renaissance, Research and Advanced Technology for Digital Libraries, Lecture Notes in Computer Science, pp.1-13, 2000.
DOI : 10.1007/3-540-45268-0_1

M. Mengucci and I. Granado, Morphological Segmentation of Text and Figures in Renaissance Books (XVI Century), Mathematical Morphology and its Applications to Image and Signal Processing Computational Imaging and Vision, pp.397-404, 2002.
DOI : 10.1007/0-306-47025-X_43

J. Y. Ramel, S. Leriche, M. L. Demonet, and S. Busson, User-driven page layout analysis of historical printed books, International Journal of Document Analysis and Recognition (IJDAR), vol.26, issue.6, pp.243-261, 2007.
DOI : 10.1007/s10032-007-0040-6

URL : https://hal.archives-ouvertes.fr/hal-00150167

A. Crasson and J. D. Fekete, Structuration des manuscrits : du corpusàcorpus`corpusà la région, Colloque International Francophone sur l'Ecrit et le Document, 2004.

K. Kise, Page segmentation techniques in document analysis. Handbook of Document Image Processing and Recognition, 2014.

B. Julesz, Visual pattern discrimination Information Theory, pp.84-92, 1962.

N. Chen and D. Blostein, A survey of document image classification: problem statement, classifier architecture and performance evaluation, International Journal of Document Analysis and Recognition (IJDAR), vol.18, issue.6, pp.1-16, 2007.
DOI : 10.1007/s10032-006-0020-2

N. Journet, J. Ramel, R. Mullot, and V. Eglin, Document image characterization using a multiresolution analysis of the texture: application to old documents, International Journal of Document Analysis and Recognition (IJDAR), vol.52, issue.6, pp.9-18, 2008.
DOI : 10.1007/s10032-008-0064-6

URL : https://hal.archives-ouvertes.fr/hal-00480087

M. Mehri, P. Héroux, P. Gomez-krämer, and R. Mullot, A Pixel Labeling Approach for Historical Digitized Books, 2013 12th International Conference on Document Analysis and Recognition, pp.817-821, 2013.
DOI : 10.1109/ICDAR.2013.167

URL : https://hal.archives-ouvertes.fr/hal-00934585

R. Cohen, A. Asi, K. Kedem, J. El-sana, and I. Dinstein, Robust text and drawing segmentation algorithm for historical documents, Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing, HIP '13, pp.110-117, 2013.
DOI : 10.1145/2501115.2501117

H. P. Lai, M. Visani, A. Boucher, and J. M. Ogier, An experimental comparison of clustering methods for content-based indexing of large image databases, Pattern Analysis and Applications, vol.78, issue.336, pp.345-366, 2012.
DOI : 10.1007/s10044-011-0261-7

URL : https://hal.archives-ouvertes.fr/hal-00859304

B. Allier, J. Duong, A. Gagneux, P. Mallet, and H. Emptoz, Texture feature characterization for logical pre-labeling, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings., pp.567-571, 2003.
DOI : 10.1109/ICDAR.2003.1227728

URL : https://hal.archives-ouvertes.fr/hal-01503067

A. K. Jain, R. P. Duin, and J. Mao, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.1, pp.4-37, 2000.
DOI : 10.1109/34.824819

Y. Liua, S. Wub, and X. Zhoua, <title>Texture segmentation based on features in wavelet domain for image retrieval</title>, Visual Communications and Image Processing 2003, pp.2026-2034, 2003.
DOI : 10.1117/12.503702

A. K. Jain, S. K. Bkattacharjee, and Y. Chen, On texture in document images, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.677-680, 1992.
DOI : 10.1109/CVPR.1992.223203

C. H. Chen, L. F. Pau, and P. Wang, Texture analysis in the handbook of pattern recognition and computer vision, 1998.

M. Tuceryan and A. K. Jain, Texture analysis. The Handbook of Pattern Recognition and Computer Vision, 1998.

R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973.
DOI : 10.1109/TSMC.1973.4309314

M. Tuceryan and A. K. Jain, Texture segmentation using Voronoi polygons Pattern Analysis and Machine Intelligence, pp.211-216, 1990.

J. Lafferty, A. Mccallum, and F. Pereira, Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data, International Conference on Machine Learning, pp.282-289, 2001.

S. Nicolas, Y. Kessentini, T. Paquet, and L. Heutte, Handwritten document segmentation using hidden Markov random fields, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), pp.212-216, 2005.
DOI : 10.1109/ICDAR.2005.124

URL : https://hal.archives-ouvertes.fr/hal-00509212

R. Chellappa and S. Chatterjee, Classification of textures using Markov random field models, ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.694-697, 1984.
DOI : 10.1109/ICASSP.1984.1172634

R. Ferrell, S. Gleason, and K. Tobin, Application of fractal encoding techniques for image segmentation, Sixth International Conference on Quality Control by Artificial Vision, pp.69-77, 2003.
DOI : 10.1117/12.514943

T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, " Pattern Analysis and Machine Intelligence, pp.971-987, 2002.

A. K. Jain and S. Bhattacharjee, Text segmentation using gabor filters for automatic document processing, Machine Vision and Applications, pp.169-184, 1992.
DOI : 10.1007/BF02626996

C. Sabharwal and S. Subramanya, Indexing image databases using wavelet and discrete Fourier transform, Proceedings of the 2001 ACM symposium on Applied computing , SAC '01, pp.434-439, 2001.
DOI : 10.1145/372202.372395

S. G. Mallat, A theory for multiresolution signal decomposition: The wavelet representation Pattern Analysis and Machine Intelligence, pp.674-693, 1989.

M. Tuceryan, Moment based texture segmentation, Pattern Recognition Letters, pp.659-668, 1994.

S. Uttama, P. Loonis, M. Delalandre, and J. M. Ogier, Segmentation and Retrieval of Ancient Graphic Documents, International Workshop on Graphics Recognition on Graphics Recognition (GREC): Ten Years Review and Future Perspectives, pp.88-98, 2006.
DOI : 10.1007/11767978_8

M. Mehri, P. Gomez-krämer, P. Héroux, and R. Mullot, Old document image segmentation using the autocorrelation function and multiresolution analysis, Document Recognition and Retrieval XX, 2013.
DOI : 10.1117/12.2002365

URL : https://hal.archives-ouvertes.fr/hal-00787779

R. M. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, pp.786-804, 1979.
DOI : 10.1109/PROC.1979.11328

M. Petrou and P. G. Sevilla, Image processing: Dealing with texture, 2006.
DOI : 10.1002/047003534X

V. Eglin, S. Bres, and C. Rivero, Hermite and Gabor transforms for noise reduction and handwriting classification in ancient manuscripts, International Journal of Document Analysis and Recognition (IJDAR), vol.33, issue.3, pp.101-122, 2007.
DOI : 10.1007/s10032-007-0039-z

A. Garz and R. Sablatnig, Multi-scale texture-based text recognition in ancient manuscripts, 2010 16th International Conference on Virtual Systems and Multimedia, pp.336-339, 2010.
DOI : 10.1109/VSMM.2010.5665938

C. Grana, D. Borghesani, and R. Cucchiara, Automatic segmentation of digitalized historical manuscripts, Multimedia Tools and Applications, pp.483-506, 2011.
DOI : 10.1007/s11042-010-0561-8

A. Ouji, Y. Leydier, and F. Lebourgeois, Chromatic / Achromatic Separation in Noisy Document Images, 2011 International Conference on Document Analysis and Recognition, pp.167-171, 2011.
DOI : 10.1109/ICDAR.2011.42

URL : https://hal.archives-ouvertes.fr/hal-01354444

S. Bres, ContributionsàContributions`Contributionsà la quantification des critères de transparence et d'anisotropie par une approche globale : Application au contrôle de qualité de matériaux composites, 1994.

M. Mehri, P. Gomez-krämer, P. Héroux, A. Boucher, and R. Mullot, Texture feature evaluation for segmentation of historical document images, Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing, HIP '13, pp.102-109, 2013.
DOI : 10.1145/2501115.2501121

URL : https://hal.archives-ouvertes.fr/hal-00934571

G. Peake and T. Tan, Script and language identification from document images, " in Document Image Analysis, IEEE, pp.10-17, 1997.

A. Busch, W. W. Boles, and S. Sridharan, Texture for script identification Pattern Analysis and Machine Intelligence, pp.1720-1732, 2005.

Y. Zhu, T. Tan, and Y. Wang, Font recognition based on global texture analysis Pattern Analysis and Machine Intelligence, pp.1192-1200, 2001.

H. Ma and D. Doermann, Gabor filter based multi-class classifier for scanned document images, International Conference on Document Analysis and Recognition, pp.968-972, 2003.

A. K. Jain and Y. Zhong, Page segmentation using texture analysis, Pattern Recognition, vol.29, issue.5, pp.743-770, 1996.
DOI : 10.1016/0031-3203(95)00131-X

T. Randen and J. H. Husøy, Segmentation of text/image documents using texture approaches, 1994.

J. C. Bezdek, R. Ehrlich, and W. Full, FCM: The fuzzy c-means clustering algorithm, Computers & Geosciences, pp.191-203, 1984.
DOI : 10.1016/0098-3004(84)90020-7

F. Kovács, C. Legány, and A. Babos, Cluster validity measurement techniques Knowledge Engineering and Data Bases, International Conference on Artificial Intelligence, pp.388-393, 2006.

J. B. Macqueen, Some methods for classification and analysis of multivariate observations, Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

L. Kaufman and P. J. Rousseeuw, Finding groups in data: An introduction to cluster analysis, 1990.
DOI : 10.1002/9780470316801

G. N. Lance and W. T. Williams, A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems, The Computer Journal, vol.9, issue.4, pp.373-380, 1967.
DOI : 10.1093/comjnl/9.4.373

M. Ester, H. P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, International Conference on Knowledge Discovery and Data Mining, pp.226-231, 1996.

M. Ankerst, M. M. Breunig, H. P. Kriegel, and J. Sander, OPTICS: Ordering Points To Identify the Clustering Structure, International Conference on Management of Data, pp.49-60, 1999.

G. J. Mclachlan and T. Krishnan, The EM algorithm and extensions, 1997.

W. Wang, J. Yang, and R. Muntz, STING: A statistical information grid approach to spatial data mining, International Conference on Very Large Data, pp.186-195, 1997.

G. Sheikholeslami, S. Chatterjee, and A. Zhang, WaveCluster: A multi-eesolution clustering approach for very large spatial databases, International Conference on Very Large Data, pp.428-439, 1998.

E. Smigiel, A. Bela¨?dbela¨?d, and H. Hamza, Self-organizing Maps and Ancient Documents, International Workshop on Document Analysis Systems, pp.125-134, 2004.
DOI : 10.1007/978-3-540-28640-0_12

URL : https://hal.archives-ouvertes.fr/inria-00100146

J. F. Rosenblatt, Principles of neurodynamics, 1962.

R. Xu, Survey of Clustering Algorithms, IEEE Transactions on Neural Networks, vol.16, issue.3, pp.645-678, 2005.
DOI : 10.1109/TNN.2005.845141

J. Cocquerez and S. Philipp, Analyse d'images : filtrage et segmentation, 1995.
URL : https://hal.archives-ouvertes.fr/hal-00706168

R. Duda, P. Hart, and D. Stork, Pattern classification, 2001.

M. Cord and P. Cunningham, Machine learning techniques for multimedia case studies on organization and retrieval, series: cognitive technologies, 2008.

A. Cornuéjols and L. Miclet, Apprentissage artificiel : concepts et algorithmes, 2010.

N. Iam-on and S. Garrett, LinkCluE: A Matlab package for link-based cluster ensembles, Journal of Statistical Software, pp.1-36, 2010.

S. Ray and R. H. Turi, Determination of number of clusters in k-means clustering and application in color image segmentation, International Conference on Advances in Pattern Recognition and Digital Techniques, pp.137-143, 1999.

H. A. Moesa, D. B. , and T. Akutsu, Efficient determination of cluster boundaries for analysis of gene expression profile data using hierarchical clustering and wavelet transform, Genome Informatics, pp.132-141, 2005.

P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol.20, pp.53-65, 1987.
DOI : 10.1016/0377-0427(87)90125-7

R. Lletía, M. C. Ortiza, L. A. Sarabiab, and M. S. Sánchez, Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes, Colloquim Chemiometricum Mediterraneum, pp.87-100, 2004.

. Statsoft, Finding the right number of clusters in k-means and EM clustering: v-Fold Cross-Validation. Electronic Statistics Textbook, 2010.

Q. Zhao, M. Xu, and P. Fränti, Extending external validity measures for determining the number of clusters, 2011 11th International Conference on Intelligent Systems Design and Applications, pp.931-936, 2011.
DOI : 10.1109/ISDA.2011.6121777

K. Kryszczuk and P. Hurley, Estimation of the Number of Clusters Using Multiple Clustering Validity Indices, International Conference on Multiple Classifier Systems, pp.114-123, 2010.
DOI : 10.1007/978-3-642-12127-2_12

N. Bolshakova and F. Azuaje, Estimating the number of clusters in DNA microarray data Methods of information in medicine, pp.153-157, 2006.

J. Yu, D. Tao, J. Li, and J. Cheng, Semantic preserving distance metric learning and applications, Information Sciences, vol.281, pp.674-686, 2014.
DOI : 10.1016/j.ins.2014.01.025

M. Wang, B. Ni, X. S. Hua, and T. S. Chua, Assistive tagging, ACM Computing Surveys, vol.44, issue.4, pp.1-25, 2012.
DOI : 10.1145/2333112.2333120

J. Yu, R. Hong, M. Wang, and J. You, Image clustering based on sparse patch alignment framework, Pattern Recognition, vol.47, issue.11, pp.3512-3519, 2014.
DOI : 10.1016/j.patcog.2014.05.002

J. Yu, Y. Rui, Y. Y. Tang, and D. Tao, High-Order Distance-Based Multiview Stochastic Learning in Image Classification, IEEE Transactions on Cybernetics, vol.44, issue.12, pp.1-12, 2014.
DOI : 10.1109/TCYB.2014.2307862

M. Cote and A. B. Albu, Texture sparseness for pixel classification of business document images, International Journal on Document Analysis and Recognition (IJDAR), vol.2, issue.3, pp.1-17, 2014.
DOI : 10.1007/s10032-014-0217-8

M. Mehri, V. C. Kieu, M. Mhiri, P. Héroux, P. Gomez-krämer et al., Robustness Assessment of Texture Features for the Segmentation of Ancient Documents, 2014 11th IAPR International Workshop on Document Analysis Systems, pp.293-297, 2014.
DOI : 10.1109/DAS.2014.22

URL : https://hal.archives-ouvertes.fr/hal-01119156

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, Systems, Man, and Cybernetics, pp.62-66, 1979.
DOI : 10.1109/TSMC.1979.4310076

L. Shijian and C. L. Tan, Script and language identification in noisy and degraded document images Pattern Analysis and Machine Intelligence, pp.14-24, 2008.

J. He, Q. D. Do, A. C. Downton, and J. H. Kim, A comparison of binarization methods for historical archive documents, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), pp.538-542, 2005.
DOI : 10.1109/ICDAR.2005.3

A. G. Lasmar, A. Kricha, and N. E. Amara, A Segmentation Text/Background Method for Degraded Ancient Arabic Manuscript, 2006 2nd International Conference on Information & Communication Technologies, pp.1327-1331, 2006.
DOI : 10.1109/ICTTA.2006.1684573

J. Li, J. Z. Wang, and G. Wiederhold, Classification of textured and non-textured images using region segmentation, Image Processing, pp.754-757, 2000.

L. Cinque, L. Lombardi, and G. Manzini, A multiresolution approach for page segmentation, Pattern Recognition Letters, vol.19, issue.2, pp.217-225, 1998.
DOI : 10.1016/S0167-8655(97)00169-4

C. Tan and P. Ng, Text extraction using pyramid, Pattern Recognition, vol.31, issue.1, pp.63-72, 1998.
DOI : 10.1016/S0031-3203(97)00026-5

C. Tan and Z. Zhang, Text block segmentation using pyramid structure, Document Recognition and Retrieval. SPIE, pp.297-306, 2000.

A. Lemaitre, J. Camillerapp, and B. Coüasnon, Multiresolution cooperation improves document structure recognition, International Journal of Document Analysis and Recognition, pp.97-109, 2008.

H. Greenspan, Multi-resolution image processing and learning for texture recognition and image enhancement, 1994.

S. Contassot-vivier, G. L. Bosco, and N. C. Dao, Multiresolution approach for image processing, 1996.

A. Kricha and N. E. Amara, Exploring textural analysis for historical documents characterization, Journal of computing, pp.24-30, 2011.

D. J. Ketchen and C. L. Shook, THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE, Strategic Management Journal, pp.441-458, 1996.
DOI : 10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G

T. Simpson, J. Armstrong, and A. Jarman, Merged consensus clustering to assess and improve class discovery with microarray data, BMC Bioinformatics, vol.11, issue.1, pp.1471-1482, 2010.
DOI : 10.1186/1471-2105-11-590

S. Monti, P. Tamayo, J. Mesirov, and T. Golub, Consensus Clustering: A resampling-based method for class discovery and visualization of gene expression microarray data, Machine Learning, pp.91-118, 2003.

G. Nguyen, M. Coustaty, and J. M. Ogier, Stroke feature extraction for lettrine indexing, 2010 2nd International Conference on Image Processing Theory, Tools and Applications, pp.355-360, 2010.
DOI : 10.1109/IPTA.2010.5586747

J. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963.
DOI : 10.1007/BF02289263

F. Lalys, C. Haegelen, M. Mehri, S. Drapier, M. Vérin et al., Anatomo-clinical atlases correlate clinical data and electrode contact coordinates: Application to subthalamic deep brain stimulation, Journal of Neuroscience Methods, vol.212, issue.2, pp.297-307, 2013.
DOI : 10.1016/j.jneumeth.2012.11.002

URL : https://hal.archives-ouvertes.fr/inserm-00750921

D. E. Knuth, The art of computer programming) sorting and searching, 1997.

P. Mahalanobis, On the generalised distance in statistics, Proceedings of the National Institute of Sciences of India. NISI, pp.49-55, 1936.

D. Doermann, E. Zotkina, and H. Li, GEDI -A Groundtruthing Environment for Document Images, International Workshop on Document Analysis Systems, 2010.

F. Ge, S. Wang, and T. Liu, New benchmark for image segmentation evaluation, Journal of Electronic Imaging, pp.1-16, 2007.

H. Zhang, J. Fritts, and S. Goldman, Image segmentation evaluation: A survey of unsupervised methods, Computer Vision and Image Understanding, vol.110, issue.2, pp.260-280, 2008.
DOI : 10.1016/j.cviu.2007.08.003

S. Wontaek, M. Agrawal, and D. Doermann, Performance Evaluation Tools for zone Segmentation and classification (PETS), International Conference on Pattern Recognition, pp.503-506, 2010.

E. Rendón, I. Abundez, A. Arizmendi, and E. M. Quiroz, Internal versus external cluster validation indexes, International Journal of Computers and Communications, pp.27-34, 2011.

E. Rendón, I. Abundez, C. Gutierrez, S. D. Zagal, A. Arizmendi et al., A comparison of internal and external cluster validation indexes, Applications of Mathematics and Computer Engineering (AMERICAN-MATH/CEA 2011). World Scientific and Engineering Academy and Society (WSEAS), pp.158-163, 2011.

A. Silva, Metrics for evaluating performance in document analysis: application to tables, International Journal on Document Analysis and Recognition (IJDAR), vol.8, issue.2-3, pp.101-109, 2011.
DOI : 10.1007/s10032-010-0144-2

J. R. Jensen, Introductory digital image processing, 1986.

P. M. Mather, Computer processing of remotely???sensed images ??? An introduction, Geocarto International, vol.2, issue.4, 1999.
DOI : 10.1080/10106048709354125

J. Makhoul, F. Kubala, R. Schwartz, and R. Weischedel, Performance measures for information extraction, DARPA Broadcast News Workshop, pp.249-252, 1999.

J. M. Wei, X. J. Yuan, Q. H. Hub, and S. Q. Wangc, A novel measure for evaluating classifiers, Expert Systems with Applications, pp.3799-3809, 2010.
DOI : 10.1016/j.eswa.2009.11.040

D. M. Powers, Evaluation: From precision, recall and F-factor to ROC, informedness, markedness & correlation, Journal of Machine Learning Technologies, pp.37-63, 2011.

B. Liu, Web data mining: Exploring hyperlinks, contents, and usage data, 2011.
DOI : 10.1007/978-3-642-19460-3

A. K. Santra and C. J. Christy, Genetic algorithm and confusion matrix for document clustering, International Journal of Computer Science, pp.322-328, 2012.

P. C. Saxena and K. Navaneetham, The Effect of Cluster Size, Dimensionality, and Number of Clusters on Recovery of True Cluster Structure Through Chernoff-Type Faces, The Statistician, vol.40, issue.4, pp.415-425, 1991.
DOI : 10.2307/2348731

E. B. Fowlkes and C. L. Mallows, A Method for Comparing Two Hierarchical Clusterings, Journal of the American Statistical Association, vol.66, issue.383, pp.553-569, 1983.
DOI : 10.1080/01621459.1983.10478008

Y. Zhao and G. Karypis, Criterion functions for document clustering: Experiments and analysis, 2001.

W. J. Krzanowski and Y. T. Lai, A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering, Biometrics, vol.44, issue.1, pp.23-34, 1988.
DOI : 10.2307/2531893

J. A. Hartigan, Classification and Clustering, Journal of Marketing Research, vol.18, issue.4, 1975.
DOI : 10.2307/3151350

R. B. Calinski and J. Harabasz, A dendrite method for cluster analysis, Communications in Statistics, pp.1-27, 1974.

W. S. Sarle, The cubic clustering criterion, SAS Institute, 1983.

A. J. Scott and M. J. Symons, Clustering Methods Based on Likelihood Ratio Criteria, Biometrics, vol.27, issue.2, pp.387-397, 1971.
DOI : 10.2307/2529003

F. H. Marriott, Practical Problems in a Method of Cluster Analysis, Biometrics, vol.27, issue.3, pp.501-514, 1971.
DOI : 10.2307/2528592

G. W. Milligan and M. Cooper, An examination of procedures for determining the number of clusters in a data set, Psychometrika, vol.77, issue.2, pp.159-179, 1985.
DOI : 10.1007/BF02294245

H. P. Friedman and J. Rubin, On Some Invariant Criteria for Grouping Data, Journal of the American Statistical Association, vol.22, issue.2, pp.1159-1178, 1967.
DOI : 10.1007/BF02289630

J. Rubin, Optimal classification into groups: An approach for solving the taxonomy problem, Journal of Theoretical Biology, vol.15, issue.1, pp.103-144, 1967.
DOI : 10.1016/0022-5193(67)90046-X

L. J. Hubert and J. R. Levin, A general statistical framework for assessing categorical clustering in free recall., Psychological Bulletin, vol.83, issue.6, pp.1072-1080, 1976.
DOI : 10.1037/0033-2909.83.6.1072

D. L. Davies and D. W. Bouldin, A cluster separation measure Pattern Analysis and Machine Intelligence, pp.224-227, 1979.

D. A. Ratkowsky and G. N. Lance, A criterion for determining the number of groups in a classification, Australian Computer Journal, pp.115-117, 1978.

G. H. Ball and D. J. Hall, ISODATA, a novel method of data analysis and pattern classification, Menlo Park: Stanford Research Institute, 1965.

G. W. Milligan, An examination of the effect of six types of error perturbation on fifteen clustering algorithms, Psychometrika, vol.45, issue.3, pp.325-342, 1980.
DOI : 10.1007/BF02293907

T. Frey and H. V. Groenewoud, A cluster analysis of the d-squared matrix of white spruce stands in saskatchewan based on the maximum-minimum principle, Journal of Ecology, pp.873-886, 1972.

J. O. Mcclain and V. R. Rao, CLUSTISZ: A program to test for the quality of clustering of a set of objects, Journal of Marketing Research, pp.456-460, 1975.

J. Dunn, Well-Separated Clusters and Optimal Fuzzy Partitions, Journal of Cybernetics, vol.4, issue.1, pp.95-104, 1974.
DOI : 10.1080/01969727408546059

M. Halkidi, M. Vazirgiannis, and I. Batistakis, Quality Scheme Assessment in the Clustering Process, Principles and Practice of Knowledge in databases, pp.265-276, 2000.
DOI : 10.1007/3-540-45372-5_26

M. Halkidi, I. Batistakis, and M. Vazirgiannis, On clustering validation techniques, Journal of Intelligent Information Systems, pp.107-145, 2001.

E. Deza and M. M. Deza, Encyclopedia of distances, 2013.

W. M. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.15, issue.336, pp.846-850, 1971.
DOI : 10.1080/01621459.1963.10500845

L. Hubert and P. Arabic, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/BF01908075

A. Kraskov, H. Stögbauer, R. G. Andrzejak, and P. Grassberger, MIC: Mutual Information Based Hierarchical Clustering, Quantitative Methods (q-bio.QM). CoRR q-bio.QM/0311039, pp.193-218, 2003.
DOI : 10.1007/978-0-387-84816-7_5

N. X. Vinh, J. Epps, and J. Bailey, Information theoretic measures for clusterings comparison, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.2837-2854, 2010.
DOI : 10.1145/1553374.1553511

D. Tao, L. Liang, L. Jin, and Y. Gao, Similar handwritten Chinese character recognition by kernel discriminative locality alignment, Pattern Recognition Letters, vol.35, pp.186-194, 2014.
DOI : 10.1016/j.patrec.2012.06.014

D. Tao, L. Jin, S. Zhang, Z. Yang, and Y. Wang, Sparse Discriminative Information Preservation for Chinese character font categorization, Neurocomputing, vol.129, pp.159-167, 2014.
DOI : 10.1016/j.neucom.2013.09.044

H. Wei, K. Chen, R. Ingold, and M. Liwicki, Hybrid Feature Selection for Historical Document Layout Analysis, 2014 14th International Conference on Frontiers in Handwriting Recognition, pp.87-92, 2014.
DOI : 10.1109/ICFHR.2014.22