Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/40544
Title: Influence of language models and candidate set size on contextual post-processing for chinese script recognition
Authors: Li, Y.-X. 
Tan, C.L. 
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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