Title :
A multi-layer classifier for recognition of unconstrained handwritten numerals
Author :
Wang, Gwo-En ; Wang, Jhing-Fa
Author_Institution :
Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
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
Keywords :
Bayes methods; handwriting recognition; image classification; multilayer perceptrons; neural nets; optical character recognition; 20 dimensional global feature vectors; fine classification; four-zone codes; handwritten numerals; hierarchical OCR system; hierarchical architecture; multilayer classifier; practical OCR system; preclassification; secondary preclassification; structural features; three layer Bayesian neural network; topological stroke features; unconstrained handwritten numeral recognition; Bayesian methods; Character recognition; Computer vision; Feature extraction; Handwriting recognition; Neural networks; Optical character recognition software; Shape; Spatial databases; Testing;
Conference_Titel :
Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-8186-7128-9
DOI :
10.1109/ICDAR.1995.602034
