Table Of ContentFundamentals in
Handwriting Recognition
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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
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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.
