A Chinese OCR spelling check approach based on statistical language models

This work describes an effective spelling check approach for Chinese OCR with a new multi-knowledge based statistical language model. This language model combines the conventional n-gram language model and the new LSA (latent semantic analysis) language model, so both local information (syntax) and...

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Bibliographic Details
Main Authors: Li Zhuang, Ta Bao, Xioyan Zhu, Chunheng Wang, Naoi, S.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Character recognition
Computer errors
Computer science
control theory
systems
Control theory. Systems
Engines
Exact sciences and technology
Image recognition
Information analysis
Natural languages
Optical character recognition software
Optical computing
Probability
Text recognition
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Description
Summary:This work describes an effective spelling check approach for Chinese OCR with a new multi-knowledge based statistical language model. This language model combines the conventional n-gram language model and the new LSA (latent semantic analysis) language model, so both local information (syntax) and global information (semantic) are utilized. Furthermore, Chinese similar characters are used in Viterbi search process to expand the candidate list in order to add more possible correct results. With our approach, the best recognition accuracy rate increases from 79.3% to 91.9%, which means 60.9% error reduction.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2004.1401278