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http://dx.doi.org/10.3745/KIPSTB.2003.10B.7.777

Machine Printed Character Recognition Based on the Combination of Recognition Units Using Multiple Neural Networks  

Lim, Kil-Taek (한국전자통신연구원 우정기술연구센터)
Kim, Ho-Yon (한국전자통신연구원 우정기술연구센터)
Nam, Yun-Seok (한국전자통신연구원 우정기술연구센터)
Publication Information
The KIPS Transactions:PartB / v.10B, no.7, 2003 , pp. 777-784 More about this Journal
Abstract
In this Paper. we propose a recognition method of machine printed characters based on the combination of recognition units using multiple neural networks. In our recognition method, the input character is classified into one of 7 character types among which the first 6 types are for Hangul character and the last type is for non-Hangul characters. Hangul characters are recognized by several MLP (multilayer perceptron) neural networks through two stages. In the first stage, we divide Hangul character image into two or three recognition units (HRU : Hangul recognition unit) according to the combination fashion of graphemes. Each recognition unit composed of one or two graphemes is recognized by an MLP neural network with an input feature vector of pixel direction angles. In the second stage, the recognition aspect features of the HRU MLP recognizers in the first stage are extracted and forwarded to a subsequent MLP by which final recognition result is obtained. For the recognition of non-Hangul characters, a single MLP is employed. The recognition experiments had been performed on the character image database collected from 50,000 real letter envelope images. The experimental results have demonstrated the superiority of the proposed method.
Keywords
Character Recognition; Neural Networks; Recognition Unit; Type Recognition;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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