Handwriting Recognition: Soft Computing and Probabilistic Approaches / Edition 1

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Overview

This book takes a fresh look at the problem of unconstrained handwriting recognition and introduces the reader to new techniques for the recognition of written words and characters using statistical and soft computing approaches. The types of uncertainties and variations present in handwriting data are discussed in detail. The book presents several algorithms that use modified hidden Markov models and Markov random field models to simulate the handwriting data statistically and structurally in a single framework. The book explores methods that use fuzzy logic and fuzzy sets for handwriting recognition. The effectiveness of these techniques is demonstrated through extensive experimental results and real handwritten characters and words.

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Product Details

  • ISBN-13: 9783540401773
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 9/10/2003
  • Series: Studies in Fuzziness and Soft Computing Series , #133
  • Edition description: 2003
  • Edition number: 1
  • Pages: 230
  • Product dimensions: 0.69 (w) x 6.14 (h) x 9.21 (d)

Table of Contents

Introduction.- Pre-processing and Feature Extraction.- Hidden Markov Model-Based Method for Recognizing Handwritten Digits.- Markov Models with Spectral Features for Handwritten Numeral Recognition.- Markov Random Field Model for Recognizing Handwritten Digits.- Markov Random Field Models for Recognizing Handwritten Words.- A Structural and Relational Approach to Handwritten Word Recognition.- Handwritten Word Recognition Using Fuzzy Logic.- Conclusion.

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