A self-organizing hierarchical classifier for multi-lingual large-set oriental character recognition
- Authors
- Park, HS; Song, HH; Lee, SW
- Issue Date
- 3월-1998
- Publisher
- WORLD SCIENTIFIC PUBL CO PTE LTD
- Keywords
- multi-lingual character recognition; Oriental character recognition; SOFM; LVQ4; language classifier
- Citation
- INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.12, no.2, pp.191 - 208
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
- Volume
- 12
- Number
- 2
- Start Page
- 191
- End Page
- 208
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/124430
- DOI
- 10.1142/S0218001498000130
- ISSN
- 0218-0014
- Abstract
- In this paper, we propose a practical scheme for multi-lingual, multi-font and multi-size large-set Oriental character recognition using a self-organizing hierarchical neural network classifier. In order to absorb the variation of the character shapes in multi-font and multi-size characters, a modified nonlinear shape normalization method based on dot density was introduced, and also to represent the different topological structures of multilingual characters effectively, a hierarchical feature extraction method was adopted. For coarse classification, a tree classifier and SOFM/LVQ based classifier which is composed of an adaptive SOFM coarse-classifier and an LVQ4 language-classifier were considered. For fine classification, a classifier based on LVQ4 learning algorithm has been developed. The experimental results revealed that the proposed scheme has the highest recognition rate of 98.27% for testing data with 7,320 kinds of multi-lingual classes and the time performance of more than 40 characters per second on 486DX-2 66MHz PC.
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