Handwriting Recognition: Soft Computing and Probabilistic Approaches / Edition 1

by Zhi-Qiang Liu, Jin-Hai Cai, Richard Buse

Overview

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, computers 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 performance 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 systems are now widely available, many transactions are still carried out in the form of cheques.

Product Details

ISBN-13:	9783642072802
Publisher:	Springer Berlin Heidelberg
Publication date:	12/07/2010
Series:	Studies in Fuzziness and Soft Computing , #133
Edition description:	Softcover reprint of hardcover 1st ed. 2003
Pages:	230
Product dimensions:	6.10(w) x 9.25(h) x 0.24(d)

Table of Contents

Introduction.- Pre-processing and Feature Extraction.- Hidden Markov Model-Based Method for Recognizing Handwritten Digits.- Markov Models with Spectral Features for Handwritten Numeral Recognition.- Markov Random Field Model for Recognizing Handwritten Digits.- Markov Random Field Models for Recognizing Handwritten Words.- A Structural and Relational Approach to Handwritten Word Recognition.- Handwritten Word Recognition Using Fuzzy Logic.- Conclusion.