Localisation and Handwriting Recognition

This report discusses several general aspects of text localisation and handwriting recognition. In particular, we consider their applications to extraction and recognition of numerals written on Giro forms. For text localisation, we investigate the problem of extracting text printed or written inside boxes on forms. We review a number of representative methods for solving this problem, describe the implementation of one of them, and present some experimental results obtained on real data. For handwriting recognition, we rst present the general methodology, including a new approach called perturbation method. Then we explain how the general methodology is applied to three subproblems in handwriting recognition, namely, the recognition of isolated numerals, that of numeral strings, and the recognition of cursively handwritten words drawn from a small lexicon. Experimental results show that our systems are either equivalent to or better than state-of-the-art systems. CR

[1]  Martin E. Hellman,et al.  The Nearest Neighbor Classification Rule with a Reject Option , 1970, IEEE Trans. Syst. Sci. Cybern..

[2]  C. K. Chow,et al.  On optimum recognition error and reject tradeoff , 1970, IEEE Trans. Inf. Theory.

[3]  Peter E. Hart,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[4]  Sahibsingh A. Dudani The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Jun S. Huang,et al.  Heuristic approach to handwritten numeral recognition , 1986, Pattern Recognit..

[6]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[7]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

[8]  Sanguklee,et al.  A comparative performance study of several global thresholding techniques for segmentation , 1990 .

[9]  Yann LeCun,et al.  Multi-Digit Recognition Using a Space Displacement Neural Network , 1991, NIPS.

[10]  Richard Lippmann,et al.  Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.

[11]  Clifford Lau,et al.  Neural Networks: Theoretical Foundations and Analysis , 1991 .

[12]  J.-C. Simon,et al.  Off-line cursive word recognition , 1992, Proc. IEEE.

[13]  Ching Y. Suen,et al.  Computer recognition of unconstrained handwritten numerals , 1992, Proc. IEEE.

[14]  Yasuaki Nakano,et al.  Segmentation methods for character recognition: from segmentation to document structure analysis , 1992, Proc. IEEE.

[15]  Haruo Asada,et al.  Major components of a complete text reading system , 1992 .

[16]  Venu Govindaraju,et al.  Separating handwritten text from interfering strokes , 1992 .

[17]  David E. Rumelhart,et al.  Self-organizing integrated segmentation and recognition neural network , 1992, Defense, Security, and Sensing.

[18]  A. J. Filipski,et al.  Automated conversion of engineering drawings to CAD form , 1992, Proc. IEEE.

[19]  K. S. Baird,et al.  Anatomy of a versatile page reader , 1992, Proc. IEEE.

[20]  Patrick J. Grother,et al.  The First Census Optical Character Recognition Systems Conference | NIST , 1992 .

[21]  Eberhard Mandler,et al.  Document analysis-from pixels to contents , 1992 .

[22]  Hirobumi Nishida,et al.  A Model-Based Split-and-Merge Method for Recognition and Segmentation of Character Strings , 1993 .

[23]  James A. Pittman,et al.  Integrated Segmentation and Recognition Through Exhaustive Scans or Learned Saccadic Jumps , 1993, Int. J. Pattern Recognit. Artif. Intell..

[24]  Horst Bunke Structural and Syntactic Pattern Recognition , 1993, Handbook of Pattern Recognition and Computer Vision.

[25]  Sargur N. Srihari,et al.  Handprinted character/digit recognition using a multiple feature/resolution philos-ophy , 1994 .

[26]  Jonathan J. Hull,et al.  A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Horst Bunke,et al.  Model-Based Analysis and Understanding of Check Forms , 1994, Int. J. Pattern Recognit. Artif. Intell..

[28]  E. Lecolinet,et al.  Strategies in character segmentation: a survey , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[29]  G. Dimauro,et al.  Segmentation of numeric strings , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[30]  Fumitaka Kimura,et al.  Handwritten ZIP code recognition using lexicon free word recognition algorithm , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[31]  Alexander Filatov,et al.  Graph-based handwritten digit string recognition , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[32]  Henry S. Baird,et al.  Document image defect models , 1995 .

[33]  Seong-Whan Lee,et al.  Integrated segmentation and recognition of connected handwritten characters with recurrent neural network , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[34]  Horst Bunke,et al.  Giro form reading machine , 1995 .

[35]  Edouard Lethelier,et al.  An automatic reading system for handwritten numeral amounts on French checks , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[36]  Horst Bunke,et al.  A system for segmenting and recognising totally unconstrained handwritten numeral strings , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[37]  Ching Y. Suen,et al.  A new system for reading handwritten zip codes , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[38]  Horst Bunke,et al.  Off-Line, Handwritten Numeral Recognition by Perturbation Method , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Fumitaka Kimura,et al.  Segmentation-recognition algorithm for zip code field recognition , 2007, Machine Vision and Applications.