research-article

Maximization of Mutual Information for Offline Thai Handwriting Recognition

Authors:

Roongroj Nopsuwanchai,

William F. ClocksinAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 28, Issue 8

Pages 1347 - 1351

https://doi.org/10.1109/TPAMI.2006.167

Published: 01 August 2006 Publication History

Abstract

This paper aims to improve the performance of an HMM-based offline Thai handwriting recognition system through discriminative training and the use of fine-tuned feature extraction methods. The discriminative training is implemented by maximizing the mutual information between the data and their classes. The feature extraction is based on our proposed block-based PCA and composite images, shown to be better at discriminating Thai confusable characters. We demonstrate significant improvements in recognition accuracies compared to the classifiers that are not discriminatively optimized.

References

[1]

H. Nishimura, M. Kobayashi, M. Maruyama, and Y. Nakano, “Off-Line Character Recognition Using HMM by Multiple Directional Feature Extraction and Voting with Bagging Algorithm,” Proc. Fifth Int'l Conf. Document Analysis and Recognition, vol. 1, pp. 49-52, 1999.

Digital Library

[2]

V.H. Quan, H. Kiem, P.N. Trung, L.T. Tin, N.D.H. Ha, and A.H. Nguyen, “A System for Recognizing Vietnamese Document Images Based on HMM and Linguistics,” Proc. Sixth Int'l Conf. Document Analysis and Recognition, pp. 627-630, 2001.

Digital Library

[3]

Computer Speech and Language, vol. 16, no. 1, pp. 25-48, 2002.

[4]

Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.

[5]

S. Airphaiboon and S. Kondo, “Recognition of Handprinted Thai Characters Using Loop Structures,” IEICE Trans. Information and Systems, vol. E79-D, no. 9, pp. 1296-1303, 1996.

[6]

P. Phokharatkul and C. Kimpan, “Handwritten Thai Character Recognition Using Fourier Descriptors and Genetic Neural Networks,” Proc. Fourth Symp. Natural Language Processing, pp. 108-123, 2000.

[7]

J.L. Mitrpanont and S. Kiwprasopsak, “The Development of the Feature Extraction Algorithms for Thai Handwritten Character Recognition System,” Lecture Notes in Computer Science, vol. 2358, pp. 536-546, June 2002.

Digital Library

[8]

Proc. IEEE, vol. 77, no. 2, pp. 257-286, 1989.

[9]

D. Povey, “Discriminative Training for Large Vocabulary Speech Recognition,” PhD thesis, Univ. of Cambridge, U.K., July 2004.

[10]

Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, pp. 1529-1532, 2001.

[11]

R. Schluter, “Investigations on Discriminative Training Criteria,” PhD thesis, RWTH Aachen, Germany, Sept. 2000.

[12]

R. Nopsuwanchai, “Discriminative Training Methods and Their Applications to Handwriting Recognition,” PhD thesis, Univ. of Cambridge, U.K., Aug. 2004.

[13]

S. Young, G. Evermann, T. Hain, D. Kershaw, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, and P. Woodland, The HTK Book (for HTK Version 3.2). Cambridge, U.K.: Cambridge Univ. Eng. Dept., 2002.

[14]

Proc. Seventh Int'l Conf. Document Analysis and Recognition, vol. 1, pp. 114-118, 2003.

[15]

J. Geist, R.A. Wilkinson, S. Janet, P.J. Grother, B. Hammond, N.W. Larsen, R.M. Klear, C.J.C. Burges, R. Creecy, J.J. Hull, T.P. Vogl, and C.L. Wilson, “The Second Census Optical Character Recognition Systems Conf.,” technical report, Nat'l Inst. of Standards and Technology, Maryland, June 1994.

Cited By

Surinta OKaraaba MSchomaker LWiering M(2015)Recognition of handwritten characters using local gradient feature descriptorsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2015.07.01745:C(405-414)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1016/j.engappai.2015.07.017
Lee HVerma B(2012)Binary segmentation algorithm for English cursive handwriting recognitionPattern Recognition10.1016/j.patcog.2011.09.01545:4(1306-1317)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1016/j.patcog.2011.09.015
Lee HVerma B(2009)Binary segmentation with neural validation for cursive handwriting recognitionProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704648(2620-2625)Online publication date: 14-Jun-2009
https://dl.acm.org/doi/10.5555/1704555.1704648

Index Terms

Maximization of Mutual Information for Offline Thai Handwriting Recognition
1. Applied computing
  1. Document management and text processing
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
  2. Machine learning

Recommendations

Indic script family and its offline handwriting recognition for characters/digits and words: a comprehensive survey
Abstract
Handwriting recognition has become an active area of research in pattern recognition and machine learning in recent years. Handwriting recognition systems have a variety of applications ranging from digital character conversion to signboard ...
Offline handwritten Gurmukhi character recognition: study of different feature-classifier combinations
DAR '12: Proceeding of the workshop on Document Analysis and Recognition

Offline handwritten character recognition (OHCR) is the method of converting handwritten text into machine processable layout. Since late sixties, efforts have been made for offline handwritten character recognition throughout the world. Principal ...
Minimum Classification Error Training for Online Handwriting Recognition

This paper describes an application of the Minimum Classification Error (MCE) criterion to the problem of recognizing online unconstrained-style characters and words. We describe an HMM-based, character and word-level MCE training aimed at minimizing ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 28, Issue 8

August 2006

172 pages

ISSN:0162-8828

Issue’s Table of Contents

Copyright © 2006.

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 August 2006

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Surinta OKaraaba MSchomaker LWiering M(2015)Recognition of handwritten characters using local gradient feature descriptorsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2015.07.01745:C(405-414)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1016/j.engappai.2015.07.017
Lee HVerma B(2012)Binary segmentation algorithm for English cursive handwriting recognitionPattern Recognition10.1016/j.patcog.2011.09.01545:4(1306-1317)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1016/j.patcog.2011.09.015
Lee HVerma B(2009)Binary segmentation with neural validation for cursive handwriting recognitionProceedings of the 2009 international joint conference on Neural Networks10.5555/1704555.1704648(2620-2625)Online publication date: 14-Jun-2009
https://dl.acm.org/doi/10.5555/1704555.1704648

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents