The recognition of handwritten numeral strings using a two-stage HMM-based method

Abstract. In this paper, a two-stage HMM-based recognition method allows us to compensate for the possible loss in terms of recognition performance caused by the necessary trade-off between segmentation and recognition in an implicit segmentation-based strategy. The first stage consists of an implicit segmentation process that takes into account some contextual information to provide multiple segmentation-recognition hypotheses for a given preprocessed string. These hypotheses are verified and re-ranked in a second stage by using an isolated digit classifier. This method enables the use of two sets of features and numeral models: one taking into account both the segmentation and recognition aspects in an implicit segmentation-based strategy, and the other considering just the recognition aspects of isolated digits. These two stages have been shown to be complementary, in the sense that the verification stage compensates for the loss in terms of recognition performance brought about by the necessary tradeoff between segmentation and recognition carried out in the first stage. The experiments on 12,802 handwritten numeral strings of different lengths have shown that the use of a two-stage recognition strategy is a promising idea. The verification stage brought about an average improvement of 9.9% on the string recognition rates. On touching digit pairs, the method achieved a recognition rate of 89.6%.

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

[2]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[3]  James D. Keeler,et al.  A Self-Organizing Integrated Segmentation and Recognition Neural Net , 1991, NIPS.

[4]  Robert Sabourin,et al.  A Time-Length Constrained Level Building Algorithm for Large Vocabulary Handwritten Word Recognition , 2001, ICAPR.

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

[6]  Hirobumi Nishida,et al.  A Model-based Split-and-Merge method for Character String Recognition , 1994, Document Image Analysis.

[7]  Seong-Whan Lee,et al.  Nonlinear shape normalization methods for the recognition of large-set handwritten characters , 1994, Pattern Recognit..

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

[9]  Sargur N. Srihari,et al.  A system for segmentation and recognition of totally unconstrained handwritten numeral strings , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[10]  Xue Wang DURATIONALLY CONSTRAINED TRAINING OF HMM WITHOUT EXPLICIT STATE DURATIONAL PDF , 1994 .

[11]  Hong Yan,et al.  Separation of single- and double-touching handwritten numeral strings , 1995 .

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

[13]  J. Makhoul,et al.  Vector quantization in speech coding , 1985, Proceedings of the IEEE.

[14]  Chinmoy B. Bose,et al.  Connected and degraded text recognition using hidden Markov model , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[15]  Flávio Bortolozzi,et al.  An Enhanced HMM Topology in an LBA Framework for the Recognition of Handwritten Numeral Strings , 2001, ICAPR.

[16]  Tetsushi Wakabayashi,et al.  Handwritten numeral recognition using autoassociative neural networks , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[17]  James Westall,et al.  Vertex directed segmentation of handwritten numerals , 1993, Pattern Recognit..

[18]  C. Suen,et al.  Improvement in handwritten numeral string recognition by slant normalization and contextual information , 2004 .

[19]  Hong Yan,et al.  Separation of single-touching handwritten numeral strings based on structural features , 1998, Pattern Recognit..

[20]  Ching Y. Suen,et al.  HMM word recognition engine , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

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

[22]  Biing-Hwang Juang,et al.  Hidden Markov Models for Speech Recognition , 1991 .

[23]  Jung-Hsien Chiang,et al.  Neural and Fuzzy Methods in Handwriting Recognition , 1997, Computer.

[24]  John Illingworth,et al.  The advantage of using an HMM-based approach for faxed word recognition , 1998, International Journal on Document Analysis and Recognition.

[25]  Richard M. Schwartz,et al.  A Script-Independent Methodology For Optical Character Recognition , 1998, Pattern Recognit..

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

[27]  Flávio Bortolozzi,et al.  A two-stage HMM-based system for recognizing handwritten numeral strings , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[28]  Luiz S. Oliveira,et al.  A NEW APPROACH TO SEGMENT HANDWRITTEN DIGITS , 2004 .

[29]  Luiz Eduardo Soares de Oliveira,et al.  A modular system to recognize numerical amounts on Brazilian bank cheques , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[30]  Jie Zhou,et al.  RECOGNITION AND VERIFICATION OF TOUCHING HANDWRITTEN NUMERALS , 2004 .

[31]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[32]  Horst Bunke,et al.  Off-line handwritten numeral string recognition by combining segmentation-based and segmentation-free methods , 1998, Pattern Recognit..

[33]  Flávio Bortolozzi,et al.  A recognition and verification strategy for handwritten word recognition , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[34]  John Illingworth,et al.  The recognition of handwritten digit strings of unknown length using hidden Markov models , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[35]  Seong-Whan Lee,et al.  Integrated segmentation and recognition of handwritten numerals with cascade neural network , 1999, IEEE Trans. Syst. Man Cybern. Part C.