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Handwriting Recognition

Soft Computing and Probabilistic Approaches

  • Zhi-Qiang Liu
  • Jinhai Cai
  • Richard Buse

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 133)

Table of contents

  1. Front Matter
    Pages N1-xv
  2. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 1-15
  3. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 17-60
  4. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 61-88
  5. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 89-105
  6. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 107-129
  7. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 131-144
  8. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 145-172
  9. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 173-193
  10. Zhi-Qiang Liu, Jinhai Cai, Richard Buse
    Pages 195-222
  11. Back Matter
    Pages 223-230

About this book

Introduction

Over the last few decades, research on handwriting recognition has made impressive progress. The research and development on handwritten word recognition are to a large degree motivated by many application areas, such as automated postal address and code reading, data acquisition in banks, text-voice conversion, security, etc. As the prices of scanners, com­ puters and handwriting-input devices are falling steadily, we have seen an increased demand for handwriting recognition systems and software pack­ ages. Some commercial handwriting recognition systems are now available in the market. Current commercial systems have an impressive performance in recognizing machine-printed characters and neatly written texts. For in­ stance, High-Tech Solutions in Israel has developed several products for container ID recognition, car license plate recognition and package label recognition. Xerox in the U. S. has developed TextBridge for converting hardcopy documents into electronic document files. In spite of the impressive progress, there is still a significant perfor­ mance gap between the human and the machine in recognizing off-line unconstrained handwritten characters and words. The difficulties encoun­ tered in recognizing unconstrained handwritings are mainly caused by huge variations in writing styles and the overlapping and the interconnection of neighboring characters. Furthermore, many applications demand very high recognition accuracy and reliability. For example, in the banking sector, although automated teller machines (ATMs) and networked banking sys­ tems are now widely available, many transactions are still carried out in the form of cheques.

Keywords

Markov algorithm algorithms fuzzy fuzzy set hidden markov model logic model

Authors and affiliations

  • Zhi-Qiang Liu
    • 1
    • 2
  • Jinhai Cai
    • 3
  • Richard Buse
    • 2
  1. 1.School of Creative MediaCity University of Hong KongKowloon, Hong KongPR China
  2. 2.Department of Computer Science and Software EngineeringThe University of MelbourneAustralia
  3. 3.School of Software Engineering & Data CommunicationsQueensland University of TechnologyBrisbaneAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-44850-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-07280-2
  • Online ISBN 978-3-540-44850-1
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site