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|Title:||Influence of language models and candidate set size on contextual post-processing for chinese script recognition||Authors:||Li, Y.-X.
|Issue Date:||2004||Citation:||Li, Y.-X.,Tan, C.L. (2004). Influence of language models and candidate set size on contextual post-processing for chinese script recognition. Proceedings - International Conference on Pattern Recognition 2 : 537-540. ScholarBank@NUS Repository.||Abstract:||In the Chinese language, a word consisting of one or more characters is a basic syntax-meaningful unit, however, each character in the word also has a definite meaning in itself. In this paper, we compare the perplexities of four n-gram language models (character-based bigram, character-based trigram, word-based bigram and class-based bigram) and their influence on the performance of contextual post-processing of Chinese scripts in an offline handwritten Chinese character recognition system. We also demonstrate the influence of the candidate set size on the performance of contextual post-processing in detail, and indicate that the number of candidates should vary with each script.||Source Title:||Proceedings - International Conference on Pattern Recognition||URI:||http://scholarbank.nus.edu.sg/handle/10635/40544||ISSN:||10514651|
|Appears in Collections:||Staff Publications|
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