Thumbnail
Access Restriction
Subscribed

Author Gwo-En Wang ♦ Jhing-Fa Wang
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1995
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Handwriting recognition ♦ Bayesian methods ♦ Neural networks ♦ Feature extraction ♦ Computer vision ♦ Optical character recognition software ♦ Shape ♦ Character recognition ♦ Spatial databases ♦ Testing
Abstract A hierarchical architecture for recognition of the unconstrained handwritten numerals is proposed. In the first stage of preclassification, a set of structural features named four-zone codes is adopted to preclassify the numerals. Due to the large degree of data and distortion of characters, it is possible to classify two different numerals with same features into a class. A secondary preclassification that utilizes topological stroke features is presented to solve this ambiguity. In order to promote the recognition rate to be a practical OCR system, a three layer Bayesian neural network with 20 dimensional global feature vectors is designed for fine classification of the confusing classes. Experimental results show that the recognition rate of the proposed hierarchical OCR system for handwritten numerals is over 99.82% based on 15423 samples.
Description Author affiliation: Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan (Gwo-En Wang)
ISBN 0818671289
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1995-08-14
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 364.62 kB
Page Count 4
Starting Page 849
Ending Page 852


Source: IEEE Xplore Digital Library