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Handwriting Recognition Soft Computing and Probabilistic Approaches

Over the last few decades, research on handwriting recognition has made impressive progress. The research and development on handwritten word recognition are to a large degree motivated by many application areas, such as automated postal address and code reading, data acquisition in banks, text-voic...

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Bibliographic Details
Main Authors: Liu, Zhi-Qiang, Cai, Jin-Hai (Author), Buse, Richard (Author)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2003, 2003
Edition:1st ed. 2003
Series:Studies in Fuzziness and Soft Computing
Subjects:
Applied Dynamical Systems
Artificial Intelligence
Instructional Psychology
Nonlinear Theories
Learning, Psychology Of
Natural Language Processing (nlp)
Applications Of Mathematics
Mathematics
Automated Pattern Recognition
Natural Language Processing (computer Science)
Pattern Recognition Systems
Dynamics
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1 Introduction
  • 1.1 Feature Extraction Methods
  • 1.2 Pattern Recognition Methods
  • 2 Pre-processing and Feature Extraction
  • 2.1 Pre-processing of Handwritten Images
  • 2.2 Feature Extraction from Binarized Images
  • 2.3 Feature Extraction Using Gabor Filters
  • 2.4 Concluding Remarks
  • 3 Hidden Markov Model-Based Method for Recognizing Handwritten Digits
  • 3.1 Theory of Hidden Markov Models
  • 3.2 Recognizing Handwritten Numerals Using Statistical and Structural Information
  • 3.3 Experimental Results
  • 3.4 Conclusion
  • 4 Markov Models with Spectral Features for Handwritten Numeral Recognition
  • 4.1 Related Work Using Contour Information
  • 4.2 Fourier Descriptors
  • 4.3 Hidden Markov Model in Spectral Space
  • 4.4 Experimental Results
  • 4.5 Discussion
  • 5 Markov Random Field Model for Recognizing Handwritten Digits
  • 5.1 Fundamentals of Markov Random Fields
  • 5.2 Markov Random Field for Pattern Recognition
  • 5.3 Recognition of Handwritten Numerals Using MRF Models
  • 5.4 Conclusion
  • 6 Markov Random Field Models for Recognizing Handwritten Words
  • 6.1 Markov Random Field for Handwritten Word Recognition
  • 6.2 Neighborhood Systems and Cliques
  • 6.3 Clique Functions
  • 6.4 Maximizing the Compatibility with Relaxation Labeling
  • 6.5 Design of Weights
  • 6.6 Experimental Results
  • 6.7 Conclusion
  • 7 A Structural and Relational Approach to Handwritten Word Recognition
  • 7.1 Introduction
  • 7.2 Gabor Parameter Estimation
  • 7.3 Feature Extraction
  • 7.4 Conditional Rule Generation System
  • 7.5 Experimental Results
  • 7.6 Conclusion
  • 8 Handwritten Word Recognition Using Fuzzy Logic
  • 8.1 Introduction
  • 8.2 Extraction of Oriented Parts
  • 8.3 System Training
  • 8.4 Word Recognition
  • 8.5 Experimental Results
  • 8.6 Conclusion
  • 9 Conclusion
  • 9.1 Summary and Discussions
  • 9.2 Future Directions
  • 9.3 References

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