Fundamentals in Handwriting Recognition

Overview

This book focuses on the fundamental tools and ideas that are generally usedin the handwriting recognition field.
Though one of the oldest fields of computer pattern recognition, this topic is now attracting growing numbers of researchers in universities and development engineers in industry.
The volume is based on a NATO Advanced Study Institute that took place in France in 1993. The most important algorithms for data acquisition, ...
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(1994). 8vo. NF.

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Overview

This book focuses on the fundamental tools and ideas that are generally usedin the handwriting recognition field.
Though one of the oldest fields of computer pattern recognition, this topic is now attracting growing numbers of researchers in universities and development engineers in industry.
The volume is based on a NATO Advanced Study Institute that took place in France in 1993. The most important algorithms for data acquisition, preprocessing, feature extraction, and classification, andthe commonest database and devices for on-line and off-line recognition are presented. The role of parallel machines and special networks in solving problems in the field are also discussed.
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Product Details

  • ISBN-13: 9783540574507
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 4/28/1994
  • Pages: 496

Table of Contents

1: Introduction and overview of field.- Frontiers in handwriting recognition.- 2: Handwritten character recognition.- Historical review of theory and practice of handwritten character recognition.- Automatic recognition of handwritten characters.- Learning, representation, understanding and recognition of characters and words — an intelligent approach.- Digital transforms in handwriting recognition.- Pattern recognition with optimal margin classifiers.- 3: Handwritten word recognition.- On the robustness of recognition of degraded line images.- Invariant handwriting features useful in cursive script recognition.- Off-line recognition of bad quality handwritten words using prototypes.- Handwriting recognition by statistical methods.- Towards a visual recognition of cursive script.- A hierarchical handwritten word segmentation.- 4: Contextual methods in handwriting recognition.- Cursive words recognition: methods and strategies.- Hidden Markov models in handwriting recognition.- Language-level syntactic and semantic constraints applied to visual word recognition.- Verification of handwritten British postcodes using address features.- Improvement of OCR by language model.- An approximate string matching method for handwriting recognition post-processing using a dictionary.- 5: Neural networks in handwriting recognition.- Neural-net computing for machine recognition of handwritten English language text.- Cooperation of feedforward neural networks for handwritten digit recognition.- Normalisation and preprocessing for a recurrent network off-line handwriting recognition system.- 6: Architectures for handwriting.- Architectures for handwriting recognition.- 7: Databases for handwriting recognition.- Large database organization for document images.- 8: Signature recognition and verification.- A model-based dynamic signature verification system.- Algorithms for signature verification.- Handwritten signature verification: a global approach.- 9: Application of handwriting recognition.- Total approach for practical character recognition system development.- A pen-based music editor.
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