Automatic writer identification framework for online handwritten documents using character prototypes

Pattern Recognition

Volumn 42, Issue 12, 2009, Pages 3313-3323

  Tan, Guo Xian ^a,b   Viard Gaudin, Christian ^b   Kot, Alex C ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INSTITUT DE RECHERCHE EN COMMUNICATIONS ET CYBERNÉTIQUE DE NANTES   (France)

Author keywords

Allographs; Fuzzy c means; Information retrieval; Online handwriting; Writer identification

Indexed keywords

ALLOGRAPHS; AUTOMATIC SEGMENTATIONS; CHARACTER LEVEL; FUZZY C MEAN; FUZZY C-MEANS; HANDWRITING RECOGNITION; HANDWRITTEN DOCUMENT; ONLINE HANDWRITING; STATISTICAL DISTRIBUTION; WRITER IDENTIFICATION;

FEEDFORWARD NEURAL NETWORKS; FUZZY CLUSTERING; FUZZY SYSTEMS; INFORMATION RETRIEVAL; INFORMATION SERVICES;

PATTERN RECOGNITION SYSTEMS;

EID: 68249120838     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.12.019     Document Type: Article

References (41)

1. H. Srinivasan, S. Kabra, H. Chen, S. Srihari, On computing strength of evidence for writer verification, in: Ninth International Conference on Document Analysis and Recognition, ICDAR 2007, 2007, pp. 844-848.
2. L. Schomaker, Advances in writer identification and verification, in: Ninth International Conference on Document Analysis and Recognition, ICDAR 2007, 2007, pp. 1268-1273.
3. S.N. Srihari, S.-H. Cha, S. Lee, Establishing handwriting individuality using pattern recognition techniques, in: Proceedings of the Sixth International Conference on Document Analysis and Recognition, 2001, pp. 1195-1204.
4. Srihari S.N., Cha S.H., Arora H., and Lee S. Individuality of handwriting. Journal of Forensic Sciences 47 (2002) 856-872
5. Connell S.D., and Jain A.K. Writer adaptation for online handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002) 329-346
6. K. Chellapilla, P. Simard, A. Abdulkader, Allograph based writer adaptation for handwritten character recognition, in: 10th International Workshop on Frontiers in Handwriting Recognition, 2006, pp. 423-428.
7. Plamondon R., and Srihari S.N. On-line and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 63-84
8. Fujisawa H. Forty years of research in character and document recognition-an industrial perspective. Pattern Recognition 41 (2008) 2435-2446
9. S.K. Chan, C. Viard-Gaudin, Y.H. Tay, Online writer identification using character prototypes distributions, in: Proceedings of SPIE-The International Society for Optical Engineering, 2008.
10. Busch A., Boles W.W., and Sridharan S. Texture for script identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005) 1720-1732
11. Bulacu M., and Schomaker L. Text-independent writer identification and verification using textural and allographic features. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (2007) 701-717
12. Hochberg J., Kelly P., Thomas T., and Kerns L. Automatic script identification from document images using cluster-based templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 176-181
13. Schomaker L., and Bulacu M. Automatic writer identification using connected-component contours and edge-based features of uppercase western script. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (2004) 787-798
14. Pervouchine V., and Leedham G. Extraction and analysis of forensic document examiner features used for writer identification. Pattern Recognition 40 (2007) 1004-1013
15. T. Pitak, T. Matsuura, On-line writer recognition for Thai based on velocity of barycenter of pen-point movement, in: International Conference on Image Processing, ICIP '04, vol. 2, 2004, pp. 889-892.
16. A. Schlapbach, H. Bunke, Using HMM based recognizers for writer identification and verification, in: Proceedings-International Workshop on Frontiers in Handwriting Recognition, IWFHR, Tokyo, 2004, pp. 167-172.
17. R. Niels, F. Gootjen, L. Vuurpijl, Writer identification through Information retrieval: the allograph weight vector, in: International Conference on Frontiers in Handwriting Recognition, 2008, pp. 481-486.
18. Niels R., Vuurpijl L., and Schomaker L. Automatic allograph matching in forensic writer identification. International Journal of Pattern Recognition and Artificial Intelligence 21 (2007) 61-81
19. A. Bensefia, T. Paquet, L. Heutte, Information retrieval based writer identification, in: Proceedings, Seventh International Conference on Document Analysis and Recognition, 2003, pp. 946-950.
20. Bensefia A., Paquet T., and Heutte L. A writer identification and verification system. Pattern Recognition Letters 26 (2005) 2080-2092
21. Chen S.M., and Horng Y.J. Fuzzy query processing for document retrieval based on extended fuzzy concept networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 29 (1999) 96-104
22. Radecki T. Generalized Boolean methods of information retrieval. International Journal of Man-Machine Studies 18 (1983) 407-439
23. Salton G., Wong A., and Yang C.S. A vector space model for automatic indexing. Communications of the ACM 18 (1975) 613-620
24. Zois E.N., and Anastassopoulos V. Morphological waveform coding for writer identification. Pattern Recognition 33 (2000) 385-398
25. Said H.E.S., Tan T.N., and Baker K.D. Personal identification based on handwriting. Pattern Recognition 33 (2000) 149-160
26. Bensefia A., Paquet T., and Heutte L. Handwritten document analysis for automatic writer recognition. Electronic Letters on Computer Vision and Image Analysis 5 (2005) 72-86
27. Vision Objects Industrial Text Recogniser SDK MyScript Builder Help, in SDK documentation: 〈http://www.visionobjects.com/about-us/download-center/_263/myscript-products-datasheets.html〉, 2008.
28. G.X. Tan, C. Viard-Gaudin, A. Kot, Online writer identification using fuzzy c-means clustering of character prototypes, in: 11th International Conference on Frontiers in Handwriting Recognition, 2008, pp. 475-480.
29. G.X. Tan, C. Viard-Gaudin, A. Kot, A stochastic nearest neighbor character prototype approach for online writer identification, in: 19th International Conference on Pattern Recognition, 2008.
30. C. Viard-Gaudin, P.M. Lallican, S. Knerr, P. Binter, The ireste on/off (ironoff) dual handwriting database, in: IEEE International Conference Document Analysis and Recognition, 1999, pp. 455-458.
31. L. Vuurpijl, L. Schomaker, Finding structure in diversity: a hierarchical clustering method for the categorization of allographs in handwriting, in: Proceedings of the Fourth International Conference on Document Analysis and Recognition, vol. 1, 1997, pp. 387-393.
32. Guyon I., Albrecht P., Cun Y.L., Denker J., and Hubbard W. Design of a neural network character recognizer for a touch terminal. Pattern Recognition 24 (1991) 105-119
33. Han J., and Kamber M. Data Mining-Concepts and Techniques (2006), Elsevier, Amsterdam
34. Hoppner F., Klawonn F., and Runkler R.K. Fuzzy Cluster Analysis-Methods for Classification, Data Analysis and Image Recognition (1999), Wiley, New York
35. Cover T., and Thomas J. Elements of Information Theory (1991), Wiley, New York
36. Huber R.A., and Headrick A.M. Handwriting Identification-Facts and Fundamentals (1999), CRC Press, Boco Raton
37. Morris R.N. Forensic Handwriting Identification-Fundamentals, Concepts and Principals (2000), Academic Press, New York
38. Schomaker L., and Segers E. Finding features used in the human reading of cursive handwriting. International Journal on Document Analysis and Recognition 2 (1999) 13-18
39. Cha S., Yoon S., and Tappert C.C. Handwriting copybook style identification for questioned document examination. Journal of Forensic Document Examiners (2007) 1-14
40. Rosenbaum R., and Fleischmann M. Character frequency in multilingual corpus 1-part 2. Journal of Quantitative Linguistics 10 (2003) 1-39
41. A.A. Lyne, The Vocabulary of French Business Correspondence: Word Frequencies, Collocations, and Problems of Lexicometric Method, Slatkine, 1985.