Fundamentals in Handwriting Recognition PDF
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Fundamentals in Handwriting Recognition NATO ASI Series Advanced Science Institutes Series A series presenting the results of activities sponsored by the NA TO Science Committee, which aims at the dissemination of advanced scientific and technological knowledge, with a view to strengthening links between scientific communities. The Series is published by an international board of publishers in conjunction with the NATO Scientific Affairs Division A Life Sciences Plenum Publishing Corporation B Physics London and New York C Mathematical and Kluwer Academic Publishers Physical Sciences Dordrecht, Boston and London o Behavioural and Social Sciences E Applied Sciences F Computer and Springer-Verlag Systems Sciences Berlin Heidelberg New York G Ecological Sciences London Paris Tokyo Hong Kong H Cell Biology Barcelona Budapest I Global Environmental Change NATO-PCO DATABASE The electronic index to the NATO ASI Series provides full bibliographical references (with keywords and/or abstracts) to more than 30000 contributions from international scientists published in all sections of the NATO ASI Series. 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Series F: Computer and Systems Sciences Vol. 124 Fundamentals in Handwriting Recognition Edited by Sebastiano Impedovo Dipartimento di Informatica, Universita degli Studi di Bari Via Amendola 173, 1-70126 Bari, Italy Springer-Verlag Berlin Heidelberg New York London Paris Tokyo Hong Kong Barcelona Budapest Published in cooperation with NATO Scientific Affairs Division Proceedings of the NATO Advanced Study Institute on Fundamentals in Hand writing Recognition, held at ChAteau de Bonas, France, June 21.July 3,1993 CR Subject Classification (1991): 1.5.4, 1.5, 1.2.7, 1.2 ISBN-13 :978-3-642-78648-8 e-ISBN-13:978-3-642-78646-4 DOl: 10.1007/978-3-642-78646-4 CIP data applied for. This work is subject to copyright. All rights are reserved, whether the whole or part of the matenal is concerned, specifically the rights oftranslation, repnnting, reuse of dlustrations, reCitation, broadcast ing, reproduction on microfilms or in any other way, and storage in data banks Duplication of this publication or parts thereof IS permitted only under the provIsions of the German COPYright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violallons are liable for prosecution under the German COPYright Law © Springer-Verlag Berlin Heidelberg 1994 Softcover reprint of the hardcover lst edition 1994 Typesetting' Camera ready by authors SPIN: 10130653 45/3140 -5 4 3 21 0 -Printed on acid-free paper Preface 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. This NATO Advanced Study Institute on Fundamentals in Handwriting Recognition took place from June 21st to July 3rd, 1993, at Chateau de Bonas, a magnificent place near Toulouse (France), which offered the most comfortable environment for study and friendly discussion, essential for the success of this NATO AS!. The aim of the Institute was to focus on the fundamental tools and ideas that are generally used in the handwriting recognition field. The most important algorithms for data acquisition, preprocessing, feature extraction, classification, the most common data base, and devices for on-line and off-line recognition were presented. The role of parallel machines and special networks in solving problems in the field was also discussed during the school. VI A total of 78 participants attended the school: 18 lecturers and 60 students. They came from 15 different countries: Austria, Bulgaria, Canada, Czechoslovakia, France, Germany, Italy, Japan, Korea, Portugal, Russia, The Netherlands, Turkey, the United Kingdom, and the United States of America. The lectures that were presented are included in this book, which consists of nine parts, each one including one or more of the main lectures. A few papers presented by participants in the school, reporting interesting results or original comments on the subject, have also been included in the book. I am particularly grateful to NATO for supporting this Advanced Study Institute and to the Dipartimento di Informatica - University of Bari, CNR (Consiglio Nazionale delle Ricerche), Tecnopolis CSATA Novus Ortus and Elsag Bailey s.p.a. for their sponsorship. I also wish to thank Prof. Jean-Claude Simon for his helpful suggestions in organizing the Institute, Prof. Andrew Corin Downton for coordinating the referee committee and his suggestions in arranging this book, Dr. Giovanni Dimauro for his cooperation in the organization of the school, and Mrs. Francoise Simon for her constant, attentive presence during the meeting. November 1993 S. Impedovo Contents Introduction .......................................................................................................... 1 Part 1: Introduction and overview offield Frontiers in handwriting recognition ................................................................ 7 S.Impedovo Part 2: Handwritten character recognition Historical review of theory and practice of handwritten character recognition ........................................................................................... 43 S.Mori Automatic recognition of handwritten characters .......................................... 70 CY. Suen Learning, representation, understanding and recognition of characters and words -an intelligent approach ............................................... 81 P.S.P. Wang Digital transforms in handwriting recognition........ ...... ... ... ...... ... ............. ...... 113 G. Dimauro Pattern recognition with optimal margin classifiers ....................................... 147 B.E. Boser Part 3: Handwritten word recognition On the robustness of recognition of degraded line images ........................... 175 IC Simon Invariant handwriting features useful in cursive script recognition ............. 179 H. -L. Teulings VIII Off-line recognition of bad quality handwritten words using prototypes .... 199 N.D. Gorsky Handwriting recognition by statistical methods .............................................. 218 T. Caesar, 1. Gloger, A. Kaltenmeier, E. Mandler Towards a visual recognition of cursive script ................................................. 223 M. Cheriet A hierarchical handwritten word segmentation .............................................. 228 G.F. Houle Part 4: Contextual methods in handwriting recognition Cursive words recognition: methods and strategies ....................................... 235 E. Lecolinet, O. Baret Hidden Markov models in handwriting recognition ....................................... 264 M. Gilloux Language-level syntactic and semantic constraints applied to visual word recognition .................................................................................. 289 1.1 Hull Verification of handwritten British postcodes using address features ........ 313 Hendrawan, A.C. Downton Improvement of OCR by language model ....................................................... 318 1. G. Koh, ID. Kim, 1 W. Lee, M.R Han, M. G. Koh, H.B. leon, H.I. Min, S. Y. Kim An approximate string matching method for handwriting recognition post-processing using a dictionary ................................................ 323 D.T. Dimov Part 5: Neural networks in handwriting recognition Neural-net computing for machine recognition of handwritten English language text ........................................................................................... 335 Y.H Pao, G.H Park IX Cooperation of feedforward neural networks for handwritten digit recognition ................................................................................................... 352 D. Price, S. Knerr Normalisation and preprocessing for a recurrent network off-line handwriting recognition system ........................................................... 360 A.W. Senior Part 6: Architectures for handwriting Architectures for handwriting recognition ...................................................... 369 A. C. Downton Part 7: Databases for handwriting recognition Large database organization for document images ........................................ 397 1.1 Hull, RK Fenrich Part 8: Signature recognition and verification A model-based dynamic signature verification system ................................... 417 R Plamondon Algorithms for signature verification ........................... ............. ...... ...... ............ 435 G. Pirlo Handwritten signature verification: a global approach .................................. 455 F. Nouboud Part 9: Application of handwriting recognition Total approach for practical character recognition system development ... 463 K Saka~ Y. Kurosawa, T. Mishima A pen-based music editor ................................................................................... 489 A. Leroy Subject Index ........................................................................................................ 495 Introduction In the first part, Sebastiano Impedovo introduces the field of handwriting recognition as a whole, outlining all the component areas where research has been undertaken over the last 20-30 years. These include data acquisition and preprocessing of both on-line and off-line handwriting, character and numeral recognition, cursive word recognition, and signature verification. All these areas are then examined in more depth in subsequent parts of the book. The field of handwritten character recognition, presented in Part 2, is the longest established branch of handwriting recognition, and therefore also the aspect which has been studied in most depth. In his historical review of research in this field, Shunji Mori defines character recognition in terms of four general categories of recognition difficulty. He then presents a taxonomy of key statistical and structural pattern recognition techniques which have been applied to character recognition. It is interesting to note that many of these techniques were first reported in the 1960s, and performance improvements achieved more recently are largely the result of improvements in data quality and attention to detail in implementation. In his review of developments in the automatic recognition of handwritten characters, Ching Suen pays close attention to human mechanisms of character recognition, since this process provides useful guidance in the choice of features for character recognition which are invariant to common forms of handwriting distortion. He also notes the improvements in performance which have recently been reported by several groups of researchers, investigating combined algorithms based upon the' use of several independent 'experts'. It seems possible that such approaches will soon be able to recognise characters better than humans in a context-free situation. Patrick Wang then broadens the field of character recognition to include artificial intelligence techniques and semantic networks, as well as introducing the particular problem of recognising Chinese characters. Giovanni Dimauro focuses on one particular class of feature space, that provided by orthogonal transforms, and particularly Discrete Fourier Transforms. The application of such transforms is illustrated using a number of examples from the character recognition field. Finally, Bernhard Boser focuses on classification methods which can be applied once features have been extracted from the character, and, after reviewing the field generally, presents a specific approach - the optimal margin classifier, developed as part of his own research.
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