IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Transactions on Asian Language Information Processing (TALIP)
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Data Structure Using Hashing and Tries For Efficient Chinese Lexical Access
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Performance Improvement Techniques for Chinese Character Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Detection of language (model) errors
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
A multiple classifier approach to detect Chinese character recognition errors
Pattern Recognition
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Two statistical language models have been investigated on their effectiveness in upgrading the accuracy of a Chinese character recognizer. The baseline model is one of lexical analytic nature which segments a sequence of character images according to the maximum matching of words with consideration of word binding forces. A model of bigram statistics of word-classes is then investigated and compared against the baseline model in terms of recognition rate improvement on the image recognizer. On the average, the baseline language model improves the recognition rate by about 7% while the bigram statistics model upgrades it by about 10%