Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

A texture-based pixel labeling approach for historical books

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Over the last few years, there has been tremendous growth in the automatic processing of digitized historical documents. In fact, finding reliable systems for the interpretation of ancient documents has been a topic of major interest for many libraries and the prime issue of research in the document analysis community. One important challenge is to refine well-known approaches based on strong a priori knowledge (e.g., the document image content, layout, typography, font size and type, scanning resolution, image size, etc.). Nevertheless, a texture analysis approach has consistently been chosen to segment a page layout when information is lacking on document structure and content. Thus, in this article, a framework is proposed to investigate the use of texture as a tool for automatically determining homogeneous regions in a digitized historical book and segmenting its contents by extracting and analyzing texture features independently of the layout of the pages. The proposed framework is parameter free and applicable to a large variety of ancient of books. It does not assume a priori information regarding document image content and structure. It consists of two phases: a texture-based feature extraction step and unsupervised clustering and labeling task based on the consensus clustering, hierarchical ascendant classification, and nearest neighbor search algorithms. The novelty of this work lies in the clustering of extracted texture descriptors to find automatically homogeneous regions, i.e., graphic and textual regions, using the clustering approach on an entire book instead of processing each page individually. Our framework has been evaluated on a large variety of historical books and achieved promising results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Notes

  1. http://www.culture.gouv.fr/culture/mrt/numerisation/

  2. http://www.archives.gov/digitization/

  3. https://diuf.unifr.ch/main/hisdoc/

  4. http://www.docexplore.eu

  5. http://www.europeana.eu

  6. http://cordis.europa.eu/libraries/en/projects/debora.html

  7. http://www.ilc.cnr.it/viewpage.php/sez=ricerca/id=97/vers=ing

  8. http://madonne.univ-lr.fr

  9. http://navidomass.univ-lr.fr

  10. http://www3.unil.ch/BCUTodai/app/todaiGetIntro.do?uri=todaiInfo&page=todaiLogo.html

  11. http://liris.cnrs.fr/graphem/

  12. http://gallica.bnf.fr

  13. http://www.bl.uk

  14. http://www.jfklibrary.org/

  15. http://ciir.cs.umass.edu/irdemo/hw-demo/

  16. http://ciir.cs.umass.edu/

  17. http://www.nsf.gov/

  18. http://memory.loc.gov/ammem/gwhtml/gwhome.html

  19. http://www.loc.gov/

  20. http://www.agence-nationale-recherche.fr/en/

  21. The DIGIDOC project is referenced under \(ANR-10-CORD-0020\). For more details, http://www.agence-nationale-recherche.fr/en/anr-funded-project/?tx_lwmsuivibilan_pi2[CODE]=ANR-10-CORD-0020

  22. http://www.bnf.fr/fr/acc/x.accueil.html

  23. http://gedigroundtruth.sourceforge.net/

References

  1. André J, Chabin MA (1999) Les documents anciens, Document Numérique

  2. LeBourgeois F, Trinh E, Allier B, Eglin V, Emptoz H (2004) Document images analysis solutions for digital libraries. In: International workshop on document image analysis for libraries. IEEE, New York, pp 2–24

  3. LeBourgeois F, Emptoz H (2007) DEBORA: Digital AccEss to BOoks of the RenAissance. Int J Doc Anal Recognit 193–221

  4. Baechler M, Fischer A, Naji N, Ingold R, Bunke H, Savoy J (2012) HisDoc: historical document analysis, recognition, and retrieval. In: Digital humanities—international conference of the alliance of digital humanities organizations (ADHO)

  5. Ogier JM, Tombre K (2006) Madonne: document image analysis techniques for cultural heritage documents. In: International conference on digital cultural heritage

  6. Rath TM, Manmatha R (2007) Word spotting for historical documents. Int J Doc Anal Recognit 139–152

  7. Baird HS (2003) Digital libraries and document image analysis. In: International conference on document analysis and recognition. IEEE, New York, pp 2–14

  8. Ogier JM (2005) Ancient document analysis: a set of new research problems. In: Colloque international francophone sur l’Ecrit et le document

  9. Coustaty M, Raveaux R, Ogier JM (2011) Historical document analysis: a review of French projects and open issues. In: European signal processing conference, EURASIP, pp 1445–1449

  10. Okun O, Pietikäinen M (1999) A survey of texture-based methods for document layout analysis. In: Workshop on texture analysis in machine vision. Springer, Berlin, pp 137–148

  11. Piper A (2013) Reading’s refrain: from bibliography to topology. In: Readings: selected essays from the English Institute, pp 373–399

  12. Nalisnick ET, Baird HS (2013) Extracting sentiment networks from Shakespeare’s plays. In: International conference on document analysis and recognition. IEEE, New York, pp 758–762

  13. Agam G, Bal G, Frieder G, Frieder O (2007) Degraded document image enhancement. In: Document recognition and retrieval. SPIE, UK

  14. Likforman-Sulem L (2003) Apport du traitement des images à la numérisation des documents anciens. Document Numérique, pp 13–26

  15. André J, Richy H, Likforman-Sulem L, Ventabert G (1999) Electronic representation and use of old documents (texts and images): about philectre project experiments. Document Numérique, pp 57–73

  16. Likforman-Sulem L, Zahour A, Taconet B (2007) Text line segmentation of historical documents: a survey. Int J Doc Anal Recognit 9:123–138

    Article  Google Scholar 

  17. Nagy G, Seth S (1984) Hierarchical representation of optically scanned documents. In: International conference on pattern recognition. IEEE, New York, pp 347–349

  18. Wahl FM, Wong KY, Casey RG (1982) Block segmentation and text extraction in mixed text/image documents. Comput Graph Image Process 20:375–390

    Article  Google Scholar 

  19. Zhou YP, Tan CL (2000) Hough technique for bar charts detection and recognition in document images. In: International conference on image processing. IEEE, New York, pp 605–608

  20. Belaïd A, Ouwayed N (2011) Guide to OCR for Arabic scripts: segmentation of ancient Arabic documents. Springer, Berlin

    Google Scholar 

  21. Nikolaou N, Makridis M, Gatos B, Stamatopoulos N, Papamarkos N (2010) Segmentation of historical machine-printed documents using adaptive run-length smoothing and skeleton segmentation paths. Imag Vis Comput 28:590–604

    Article  Google Scholar 

  22. Serra J (1982) Image analysis and mathematical morphology. Academic Press, London

    MATH  Google Scholar 

  23. Granado I, Mengucci M, Muge F (2000) Extraction de textes et de figures dans les livres anciens à l’aide de la morphologie mathématique. In: Colloque International Francophone sur l’Ecrit et le Document

  24. Muge F, Granado I, Mengucci M, Pina P, Ramos V, Sirakov N, Pinto JRC, Marcolino A, Ramalho M, Vieira P, Amaral AMD (2000) Automatic feature extraction and recognition for digital access of books of the Renaissance. In: Research and advanced technology for digital libraries. Lecture notes in computer science. Springer, Berlin, pp 1–13

  25. Mengucci M, Granado I (2002) Morphological segmentation of text and figures in renaissance books (XVI century). In: Mathematical morphology and its applications to image and signal processing computational imaging and vision, pp 397–404

  26. Ramel JY, Leriche S, Demonet ML, Busson S (2007) User-driven page layout analysis of historical printed books. Int J Doc Anal Recognit 9:243–261

    Article  Google Scholar 

  27. Crasson A, Fekete JD (2004) Structuration des manuscrits: du corpus à la région. In: Colloque International Francophone sur l’Ecrit et le Document

  28. Kise K (2014) Page segmentation techniques in document analysis. In: Handbook of document image processing and recognition. Springer, Berlin

  29. Julesz B (1962) Visual pattern discrimination. Inf Theory 8:84–92

    Article  Google Scholar 

  30. Chen N, Blostein D (2007) A survey of document image classification: problem statement, classifier architecture and performance evaluation. Int J Doc Anal Recognit 10:1–16

    Article  Google Scholar 

  31. Journet N, Ramel J, Mullot R, Eglin V (2008) Document image characterization using a multiresolution analysis of the texture: application to old documents. Int J Doc Anal Recognit 11:9–18

    Article  Google Scholar 

  32. Mehri M, Héroux P, Gomez-Krämer P, Mullot R (2013) A pixel labeling approach for historical digitized books. In: International conference on document analysis and recognition. IEEE, New York, pp 817–821

  33. Cohen R, Asi A, Kedem K, El-Sana J, Dinstein I (2013) Robust text and drawing segmentation algorithm for historical documents. In: International workshop on historical document imaging and processing. ACM, New York, pp 110–117

  34. Lai HP, Visani M, Boucher A, Ogier JM (2012) An experimental comparison of clustering methods for content-based indexing of large image databases. Pattern Anal Appl 15:345–366

    Article  MathSciNet  Google Scholar 

  35. Allier B, Duong J, Gagneux A, Mallet P, Emptoz H (2003) Texture feature characterization for logical pre-labeling. In: International conference on document analysis and recognition. IEEE, New York, pp 567–571

  36. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. Pattern Anal Mach Intell 22:4–37

    Article  Google Scholar 

  37. Liua Y, Wub S, Zhoua X (2003) Texture segmentation based on features in wavelet domain for image retrieval, pp 2026–2034

  38. Jain AK, Bkattacharjee SK, Chen Y (1992) On texture in document images. In: Computer vision and pattern recognition. IEEE, New York, pp 677–680

  39. Chen CH, Pau LF, Wang P (1998) Texture analysis in the handbook of pattern recognition and computer vision, 2nd edn. World Scientific, Singapore

    Google Scholar 

  40. Tuceryan M, Jain AK (1998) Texture analysis. In: Chen CH, Pau LF, Wang PSP (eds) The handbook of pattern recognition and computer vision, 2nd edn. World Scientific Publishing Co, Singapore

    Google Scholar 

  41. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. Syst Man Cybern 3:610–621

    Article  Google Scholar 

  42. Tuceryan M, Jain AK (1990) Texture segmentation using Voronoi polygons. Pattern Anal Mach Intell 12:211–216

    Article  Google Scholar 

  43. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: International conference on machine learning, pp 282–289

  44. Nicolas S, Kessentini Y, Paquet T, Heutte L (2005) Handwritten document segmentation using hidden Markov random fields. In: International conference on document analysis and recognition. IEEE, New York, pp 212–216

  45. Chellappa R, Chatterjee S (1984) Classification of textures using Markov random field models. In: International conference on acoustics, speech, and signal processing. IEEE, New York, pp 694–697

  46. Ferrell R, Gleason S, Tobin K (2003) Application of fractal encoding techniques for image segmentation. In: International conference on quality control by artificial vision. SPIE, Bellingham, pp 69–77

  47. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Anal Mach Intell 24:971–987

    Article  MATH  Google Scholar 

  48. Jain AK, Bhattacharjee S (1992) Text segmentation using Gabor filters for automatic document processing. Mach Vis Appl 5:169–184

    Article  Google Scholar 

  49. Sabharwal C, Subramanya S (2001) Indexing image databases using wavelet and discrete Fourier transform. In: Symposium on applied computing. ACM, New York, pp 434–439

  50. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. Pattern Anal Mach Intell 11:674–693

    Article  MATH  Google Scholar 

  51. Tuceryan M (1994) Moment based texture segmentation. Pattern Recognit Lett 15:659–668

    Article  Google Scholar 

  52. Uttama S, Loonis P, Delalandre M, Ogier JM (2006) Segmentation and retrieval of ancient graphic documents. In: International workshop on graphics recognition on graphics recognition (GREC): ten years review and future perspectives. Springer, Berlin, pp 88–98

  53. Mehri M, Gomez-Krämer P, Héroux P, Mullot R (2013) Old document image segmentation using the autocorrelation function and multiresolution analysis. In: Document recognition and retrieval. SPIE, Bellingham

  54. Haralick RM (1979) Statistical and structural approaches to texture. In: Proceedings of the IEEE, pp 786–804

  55. Petrou M, Sevilla PG (2006) Image processing: dealing with texture. Wiley, New York

    Book  Google Scholar 

  56. Eglin V, Bres S, Rivero C (2007) Hermite and Gabor transforms for noise reduction and handwriting classification in ancient manuscripts. Int J Doc Anal Recognit 9:101–122

    Article  Google Scholar 

  57. Garz A, Sablatnig R (2010) Multi-scale texture-based text recognition in ancient manuscripts. In: International conference on virtual systems and multimedia. IEEE, New York, pp 336–339

  58. Grana C, Borghesani D, Cucchiara R (2011) Automatic segmentation of digitalized historical manuscripts. Multimed Tools Appl 55:483–506

    Article  Google Scholar 

  59. Ouji A, Leydier Y, LeBourgeois F (2011) Chromatic/achromatic separation in noisy document images. In: International conference on document analysis and recognition. IEEE, New York, pp 167–171

  60. Bres S (1994) Contributions à la quantification des critères de transparence et d’anisotropie par une approche globale : Application au contrôle de qualité de matériaux composites. Ph.D. dissertation, Institut National des Sciences Appliquées de Lyon, Lyon, France

  61. Mehri M, Gomez-Krämer P, Héroux P, Boucher A, Mullot R (2013) Texture feature evaluation for segmentation of historical document images. In: International workshop on historical document imaging and processing. ACM, New York, pp 102–109

  62. Mehri M, Gomez-Krämer P, Héroux P, Boucher A, Mullot R (2014) A pixel labeling framework for comparing texture features: application to digitized ancient books. In: International conference on pattern recognition applications and methods. SciTePress, Canada, pp 553–560

  63. Peake G, Tan T (1997) Script and language identification from document images. In: Document image analysis. IEEE, New York, pp 10–17

  64. Busch A, Boles WW, Sridharan S (2005) Texture for script identification. Pattern Anal Mach Intell 27:1720–1732

    Article  Google Scholar 

  65. Zhu Y, Tan T, Wang Y (2001) Font recognition based on global texture analysis. Pattern Anal Mach Intell 23:1192–1200

    Article  Google Scholar 

  66. Ma H, Doermann D (2003) Gabor filter based multi-class classifier for scanned document images. In: International conference on document analysis and recognition. IEEE, New York, pp 968–972

  67. Jain AK, Zhong Y (1996) Page segmentation using texture analysis. Pattern Recognit 29:743–770

    Article  Google Scholar 

  68. Randen T, Husøy JH (1994) Segmentation of text/image documents using texture approaches

  69. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy C-means clustering algorithm. In: Computers and geosciences. Pergamon Press, UK, pp 191–203

  70. Kovács F, Legány C, Babos A (2006) Cluster validity measurement techniques. In: International conference on artificial intelligence, knowledge engineering and data bases. World Scientific and Engineering Academy and Society, Greece, pp 388–393

  71. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Berkeley symposium on mathematical statistics and probability. University of California Press, California, pp 281–297

  72. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Book  MATH  Google Scholar 

  73. Lance GN, Williams WT (1967) A general theory of classificatory sorting strategies 1. Hierarchical systems. Comput J 9:373–380

    Article  Google Scholar 

  74. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: International conference on knowledge discovery and data mining. AAAI Press, Palo Alto, pp 226–231

  75. Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: International conference on management of data. ACM Press, New York, pp 49–60

  76. McLachlan GJ, Krishnan T (1997) The EM algorithm and extensions. Wiley, New York

    MATH  Google Scholar 

  77. Wang W, Yang J, Muntz R (1997) STING: a statistical information grid approach to spatial data mining. In: International conference on very large data. Morgan Kaufmann, Burlington, pp 186–195

  78. Sheikholeslami G, Chatterjee S, Zhang A (1998) WaveCluster: a multi-eesolution clustering approach for very large spatial databases. In: International conference on very large data. Morgan Kaufmann, Burlington, pp 428–439

  79. Smigiel E, Belaïd A, Hamza H (2004) Self-organizing maps and ancient documents. In: International workshop on document analysis systems. Springer, Berlin, pp 125–134

  80. Rosenblatt JF (1962) Principles of neurodynamics. Spartan Books, Italy

  81. Xu R (2005) Survey of clustering algorithms. Neural Netw 16:645–678

    Article  Google Scholar 

  82. Cocquerez J, Philipp S (1995) Analyse d’images: filtrage et segmentation, Masson

  83. Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. Wiley-Interscience, New York

    MATH  Google Scholar 

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

    Book  Google Scholar 

  85. Cornuéjols A, Miclet L (2010) Apprentissage artificiel: concepts et algorithmes, 2nd edn. Eyrolles, Paris

  86. Iam-on N, Garrett S (2010) LinkCluE: a Matlab package for link-based cluster ensembles. J Stat Softw 36:1–36

    Article  Google Scholar 

  87. Ray S, Turi RH (1999) Determination of number of clusters in k-means clustering and application in color image segmentation. In: International conference on advances in pattern recognition and digital techniques. Narosa Publishing House, Chennai, pp 137–143

  88. Moesa HA, Akutsu DBKCT (2005) Efficient determination of cluster boundaries for analysis of gene expression profile data using hierarchical clustering and wavelet transform. Genome Inform 16:132–141

  89. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  MATH  Google Scholar 

  90. Lletía R, Ortiza MC, Sarabiab LA, Sánchez MS (2004) Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes. In: Colloquim Chemiometricum Mediterraneum. Elsevier Science, Analytica Chimica Acta, pp 87–100

  91. StatSoft (2010) Finding the right number of clusters in k-means and EM clustering: v-fold cross-validation. In: Electronic statistics textbook (Online). http://www.statsoft.com/textbook/cluster-analysis/

  92. Q. Zhao, M. Xu, P. Fränti (2011) Extending external validity measures for determining the number of clusters. In: International conference on intelligent systems design and applications. IEEE, New York, pp 931–936

  93. Kryszczuk K, Hurley P (2010) Estimation of the number of clusters using multiple clustering validity indices. In: International conference on multiple classifier systems. Springer, Berlin, pp 114–123

  94. Bolshakova N, Azuaje F (2006) Estimating the number of clusters in DNA microarray data. In: Methods of information in medicine, pp 153–157

  95. Cote M, Albu AB (2014) Texture sparseness for pixel classification of business document images. Int J Doc Anal Recognit, 1–17

  96. Mehri M, Kieu VC, Mhiri M, Héroux P, Gomez-Krämer P, Mahjoub MA, Mullot R (2014) Robustness assessment of texture features for the segmentation of ancient documents. In: International workshop on document analysis systems. IEEE, New York, pp 293–297

  97. Otsu N (1979) A threshold selection method from gray-level histograms. Syst Man Cybern 62–66

  98. Shijian L, Tan CL (2008) Script and language identification in noisy and degraded document images. Pattern Anal Mach Intell, 14–24

  99. He J, Do QDM, Downton AC, Kim JH (2005) A comparison of binarization methods for historical archive documents. In: International conference on document analysis and recognition. IEEE, New York, pp 538–542

  100. Lasmar AG, Kricha A, Amara NEB (2006) A segmentation text/background method for degraded ancient Arabic manuscript. In: International conference on information & communication technologies. IEEE, New York, pp 1327–1331

  101. Li J, Wang JZ, Wiederhold G (2000) Classification of textured and non-textured images using region segmentation. Image Process, 754–757

  102. Cinque L, Lombardi L, Manzini G (1998) A multiresolution approach for page segmentation. Pattern Recognit Lett, 217–225

  103. Tan C, Ng P (1998) Text extraction using pyramid. Pattern Recognit, 63–72

  104. Tan C, Zhang Z (2000) Text block segmentation using pyramid structure. In: Document recognition and retrieval. SPIE, UK, pp 297–306

  105. Lemaitre A, Camillerapp J, Coüasnon B (2008) Multiresolution cooperation improves document structure recognition. Int J Doc Anal Recognit, 97–109

  106. Greenspan H (1994) Multi-resolution image processing and learning for texture recognition and image enhancement. Ph.D. dissertation, California Institute of Technology

  107. Contassot-Vivier S, Bosco GL, Dao NC (1996) Multiresolution approach for image processing. In: Erasmus ICP-A-2007

  108. Kricha A, Amara NEB (2011) Exploring textural analysis for historical documents characterization. J comput, 24–30

  109. Ketchen DJ, Shook CL (1996) The application of cluster analysis in strategic management research: an analysis and critique. Strateg Manag J, 441–458

  110. Simpson T, Armstrong J, Jarman A (2010) Merged consensus clustering to assess and improve class discovery with microarray data. Boston Med Center Bioinf, 1471–1482

  111. Monti S, Tamayo P, Mesirov J, Golub T (2003) Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn, 91–118

  112. Nguyen G, Coustaty M, Ogier JM (2010) Stroke feature extraction for lettrine indexing. In: International conference on image processing theory tools and applications. IEEE, New York, pp 355–360

  113. Ward J (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc, 236–244

  114. Lalys F, Haegelen C, Mehri M, Drapier S, Vérin M, Jannin P (2013) Anatomo-clinical atlases correlate clinical data and electrode contact coordinates: application to subthalamic deep brain stimulation. J Neurosci, 297–307

  115. Knuth DE (1997) The art of computer programming, sorting and searching, vol 3, 2nd edn. Addison Wesley Longman Publishing Co, UK

  116. Mahalanobis P (1936) On the generalised distance in statistics. In: Proceedings of the National Institute of Sciences of India, NISI, pp 49–55

  117. Doermann D, Zotkina E, Li H (2010) GEDI—a groundtruthing environment for document images. In: International workshop on document analysis systems. ACM, New York

  118. Ge F, Wang S, Liu T (2007) New benchmark for image segmentation evaluation. J Electron Imag, 1–16

  119. Zhang H, Fritts J, Goldman S (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Understanding, 260–280

  120. Wontaek S, Agrawal M, Doermann D (2010) Performance evaluation tools for zone segmentation and classification (PETS). In: International conference on pattern recognition. IEEE, New York, pp 503–506

  121. Rendón E, Abundez I, Arizmendi A, Quiroz EM (2011) Internal versus external cluster validation indexes. Int J Comput Commun, 27–34

  122. Rendón E, Abundez I, Gutierrez C, Zagal SD, Arizmendi A, Quiroz EM, Arzate HE (2011) A comparison of internal and external cluster validation indexes. In: Applications of mathematics and computer engineering (AMERICAN-MATH/CEA. World Scientific and Engineering Academy and Society (WSEAS), pp 158–163

  123. Silva A (2011) Metrics for evaluating performance in document analysis: application to tables. Int J Doc Anal Recognit, 101–109

  124. Jensen JR (1986) Introductory digital image processing. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  125. Mather PM (1999) Computer processing of remotely-sensed images: an introduction, 2nd edn. Wiley, New York

  126. Makhoul J, Kubala F, Schwartz R, Weischedel R (1999) Performance measures for information extraction. In: DARPA Broadcast News Workshop. Morgan Kaufmann Publishers Inc, Burlington, pp 249–252

  127. Wei JM, Yuan XJ, Hub QH, Wang SQ (2010) A novel measure for evaluating classifiers. Exp Syst Appl, 3799–3809

  128. Powers DMW (2011) Evaluation: from precision, recall and F-factor to ROC, informedness, markedness & correlation. J Mach Learn Technol, pp 37–63

  129. Liu B (2011) Web data mining: exploring hyperlinks, contents, and usage data. Springer, Berlin

  130. Santra AK, Christy CJ (2012) Genetic algorithm and confusion matrix for document clustering. Int J Comput Sci, 322–328

  131. Saxena PC, Navaneetham K (1991) The effect of cluster size, dimensionality, and number of clusters on recovery of true cluster structure through Chernoff-type faces. J R Stat Soc Stat, 415–425

  132. Fowlkes EB, Mallows CL (1983) A method for comparing two hierarchical clusterings. J Am Stat Assoc, 553–569

  133. Zhao Y, Karypis G (2001) Criterion functions for document clustering: experiments and analysis. Department of Computer Science, University of Minnesota, Tech. Rep. Technical report TR 0140

  134. Krzanowski WJ, Lai YT (1988) A criterion for determining the number of groups in a data set using sum-of-squares clustering. International Biometric Society, JSTOR, pp 23–34

  135. Hartigan JA (1975) Clustering algorithms. Wiley, New York

  136. Calinski RB, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat, 1–27

  137. Sarle WS (1983) The cubic clustering criterion. SAS Institute, Tech. Rep. SAS technical report A-108: the cubic clustering criterion

  138. Scott AJ, Symons MJ (1971) Clustering methods based on likelihood ratio criteria. Biometrics, 387–397

  139. Marriott FH (1971) Practical problems in a method of cluster analysis. Biometrics, 501–514

  140. Milligan GW, Cooper M (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika, 159–179

  141. Friedman HP, Rubin J (1967) On some invariant criteria for grouping data. J Am Stat Assoc, pp 1159–1178

  142. Rubin J (1967) Optimal classification into groups: an approach for solving the taxonomy problem. J Theor Biol, 103–144

  143. Hubert LJ, Levin JR (1976) A general statistical framework for assessing categorical clustering in free recall. Psychol Bull, 1072–1080

  144. Davies DL, Bouldin DW (1979) A cluster separation measure. Pattern Anal Mach Intell, 224–227

  145. Ratkowsky DA, Lance GN (1978) A criterion for determining the number of groups in a classification. Aust Comput J, 115–117

  146. Ball GH, Hall DJ (1965) ISODATA, a novel method of data analysis and pattern classification. Menlo Park: Stanford Research Institute, Tech. Rep. AD0699616

  147. Milligan GW (1980) An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika, 325–342

  148. Frey T, Groenewoud HV (1972) A cluster analysis of the d-squared matrix of white spruce stands in saskatchewan based on the maximum-minimum principle. J Ecol, 873–886

  149. McClain JO, Rao VR (1975) CLUSTISZ: a program to test for the quality of clustering of a set of objects. J Market Res, 456–460

  150. Dunn J (1974) Well separated clusters and optimal fuzzy partitions. J Cybern, 95–104

  151. Halkidi M, Vazirgiannis M, Batistakis I (2000) Quality scheme assessment in the clustering process. In: Principles and practice of knowledge in databases. Springer, Berlin, pp 265–276

  152. Halkidi M, Batistakis I, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst, 107–145

  153. Deza E, Deza MM (2013) Encyclopedia of distances. Springer, Berlin

  154. Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc, 846–850

  155. Hubert L, Arabic P (1985) Comparing partitions. J Classif, 193–218

  156. Kraskov A, Stögbauer H, Andrzejak RG, Grassberger P (2003) Hierarchical clustering based on mutual information. In: Quantitative methods (q-bio.QM). CoRR q-bio.QM/0311039, 2003, pp 193–218

  157. Vinh NX, Epps J, Bailey J (2010) Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J Mach Learn Res, 2837–2854

  158. Wei H, Chen K, Ingold R, Liwicki M (2014) Hybrid feature selection for historical document layout analysis. In: International conference on frontiers in handwriting recognition. IEEE, New York, pp 87–92

Download references

Acknowledgments

The support of this research by the ANR (French National Research Agency) under contract ANR-10-CORD-0020 is gratefully acknowledged. The authors would like also to thank Geneviève CRON of the BnF for providing access to the Gallica digital library.

Author information

Authors and Affiliations

  1. L3i, University of La Rochelle, Avenue Michel Crépeau, 17042, La Rochelle, France

    Maroua Mehri, Petra Gomez-Krämer, Alain Boucher & Rémy Mullot

  2. LITIS EA 4108, University of Rouen, Avenue de l’Université, 76800, Saint-Etienne-du-Rouvray, France

    Pierre Héroux

Authors
  1. Maroua Mehri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petra Gomez-Krämer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre Héroux
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alain Boucher
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rémy Mullot
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maroua Mehri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mehri, M., Gomez-Krämer, P., Héroux, P. et al. A texture-based pixel labeling approach for historical books. Pattern Anal Applic 20, 325–364 (2017). https://doi.org/10.1007/s10044-015-0451-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-015-0451-9

Keywords

Search

Navigation