Fundamentals in Handwriting Recognition

Front Cover
Sebastiano Impedovo
Springer Science & Business Media, Dec 6, 2012 - Computers - 496 pages
For many years researchers in the field of Handwriting Recognition were considered to be working in an area of minor importance in Pattern Recog nition. They had only the possibility to present the results of their research at general conferences such as the ICPR or publish their papers in journals such as some of the IEEE series or PR, together with many other papers generally oriented to the more promising areas of Pattern Recognition. The series of International Workshops on Frontiers in Handwriting Recog nition and International Conferences on Document Analysis and Recognition together with some special issues of several journals are now fulfilling the expectations of many researchers who have been attracted to this area and are involving many academic institutions and industrial companies. But in order to facilitate the introduction of young researchers into the field and give them both theoretically and practically powerful tools, it is now time that some high level teaching schools in handwriting recognition be held, also in order to unite the foundations of the field. Therefore it was my pleasure to organize the NATO Advanced Study Institute on Fundamentals in Handwriting Recognition that had its origin in many exchanges among the most important specialists in the field, during the International Workshops on Frontiers in Handwriting Recognition.
 

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Contents

Introduction
2
S Impedovo
37
Automatic recognition of handwritten characters
70
P S P Wang
113
G Dimauro
145
On the robustness of recognition of degraded line images 175
173
H L Teulings
196
Offline recognition of bad quality handwritten words using prototypes
199
Languagelevel syntactic and semantic constraints applied
289
Verification of handwritten British postcodes using address features
313
Neuralnet computing for machine recognition of handwritten
334
Cooperation of feedforward neural networks for handwritten
352
Architectures for handwriting recognition 369
367
Large database organization for document images
397
A modelbased dynamic signature verification system 417
416
Algorithms for signature verification
435

Handwriting recognition by statistical methods
218
methods and strategies
235
Hidden Markov models in handwriting recognition
264
G Pirlo
452
K Sakai Y Kurosawa T Mishima
489
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