Handwriting Recognition Soft Computing and Probabilistic Approaches /

• Κύριοι συγγραφείς: Liu, Zhi-Qiang (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Cai, Jin-Hai (http://id.loc.gov/vocabulary/relators/aut), Buse, Richard (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.
• Έκδοση: 1st ed. 2003.
• Σειρά: Studies in Fuzziness and Soft Computing, 133
• Θέματα: Artificial intelligence.; Learning.; Instruction.; Pattern recognition.; Natural language processing (Computer science).; Applied mathematics.; Engineering mathematics.; Computational complexity.; Artificial Intelligence.; Learning & Instruction.; Pattern Recognition.; Natural Language Processing (NLP).; Applications of Mathematics.; Complexity.

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...

Πίνακας περιεχομένων:

• 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.