Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001).

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.

Knowledge-Based Intelligent Techniques in Character Recognition presents research results on intelligent character recognition techniques, reflecting the tremendous worldwide interest in the applications of knowledge-based techniques in this challenging field. This resource will interest anyone involved in computer science, computer engineering, applied mathematics, or related fields. It will also be of use to researchers, application engineers and students who wish to develop successful character recognition systems such as those used in reading addresses in a postal routing system or processing bank checks. Features

Frans Coenen University of Liverpool, UK This volume comprises the refereed technical papers presented at AI2003, the Twenty third SGAI International Conference on the theory, practice and application of Artificial Intelligence, held in Cambridge in December 2003. The conference was organised by SGAI, the British Computer Society Specialist Group on Artificial Intelligence (previously known as SGES). The papers in this volume present new and innovative developments in the field, divided into sections on Machine Learning, Knowledge Representation and Reasoning, Knowledge Acquisition, Constraint Satisfaction, Scheduling and Natural Language Processing. This year's prize for the best refereed technical paper was won by a paper entitled An Improved Hybrid Genetic Algorithm: New Results for the Quadratic Assignment Problem by A. Misevicius (Department of Practical Informatics, Kaunas University of Technology, Lithuania). SGAI gratefully acknowledges the long-term sponsorship of Hewlett-Packard Laboratories (Bristol) for this prize, which goes back to the 1980s. This is the twentieth volume in the Research and Development series. The Application Stream papers are published as a companion volume under the title Applications and Innovations in Intelligent Systems XI. On behalf of the conference organising committee I should like to thank all those who contributed to the organisation of this year's technical programme, in particular the programme committee members, the referees and our administrator Fiona Hartree and Linsay Turbert.

Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden Markov models and Markov-chain or n-gram models. First, the text introduces the typical architecture of a Markov model-based handwriting recognition system, and familiarizes the reader with the essential theoretical concepts behind Markovian models. Then, the text reviews proposed solutions in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition.

Optical character recognition and document image analysis have become very important areas with a fast growing number of researchers in the field. This comprehensive handbook with contributions by eminent experts, presents both the theoretical and practical aspects at an introductory level wherever possible.

The papers in this volume deal specifically with the problem of handwriting recognition and the new frontiers of research in the scientific community - including the significance of linguistic and contextual information in handwriting recognition and for adaptive preprocessing and postprocessing. Also addressed are problems related to human and computer behaviour in recognition. The aim of this book is not only to point out the state-of-the-art and the frontiers in the field of handwriting recognition, but also to stimulate research and ideas in the fields of design and implementation of on-line and off-line systems that recognize isolated and connected handwritten characters, words and signatures."