edited by M. Khosrow-Pour, pp. 1-6, copyright 2005 by Information Science Reference, formerly known as Idea Group Reference (an imprint of IGI Global)
Published in Chapter:
A Duplicate Chinese Document Image Retrieval System
Yung-Kuan Chan (National Chung Hsing University, Taiwan, R.O.C.), Yu-An Ho (National Chung Hsing University, Taiwan, R.O.C.), Hsien-Chu Wu (National Taichung Institute of Technology, Taiwan, R.O.C.), and Yen-Ping Chu (National Chung Hsing University, Taiwan, R.O.C.)
Copyright: © 2009
|Pages: 6
DOI: 10.4018/978-1-60566-026-4.ch190
Abstract
An optical character recognition (OCR) system enables a user to feed an article directly into an electronic computer file and translate the optically scanned bitmaps of text characters into machine-readable codes; that is, ASCII, Chinese GB, as well as Big5 codes, and then edits it by using a word processor. OCR is hence being employed by libraries to digitize and preserve their holdings. Billions of letters are sorted every day by OCR machines, which can considerably speed up mail delivery. The techniques of OCR can be divided into two approaches: template matching and structure analysis (Mori, Suen & Yamamoto, 1992). The template matching approach is to reduce the complexity of matching by projecting from two-dimensional information onto one; the structure analysis approach is to analyze the variation of shapes of characters. The template matching approach is only suitable for recognizing printed characters; however, the structure analysis approach can be applied to recognize handwritten characters. Several OCR techniques have been proposed, based on statistical, matching, transform and shape features (Abdelazim & Hashish, 1989; Papamarkos, Spilioties & Zoumadakis, 1994). Recently, integrated OCR systems have been proposed, and they take advantage of specific character- driven hardware implementations (Pereira & Bourbakis, 1995). OCR generally involves four discrete processes (Khoubyari & Hull, 1996; Liu, Tang & Suen, 1997; Wang, Fan & Wu, 1997): 1. separate the text and the image blocks; then finds columns, paragraphs, text lines, words, and characters; 2. extract the features of characters, and compare their features with a set of rules that can distinguish each character/font from others; 3. correct the incorrect words by using spell checking tools; and 4. translate each symbol into a machine-readable code.