Please use this identifier to cite or link to this item: https://doi.org/10.1109/IWFHR.2004.15
Title: An empirical study of statistical language models for contextual post-processing of Chinese script recognition
Authors: Li, Y.-X. 
Tan, C.L. 
Issue Date: 2004
Source: Li, Y.-X.,Tan, C.L. (2004). An empirical study of statistical language models for contextual post-processing of Chinese script recognition. Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR : 257-262. ScholarBank@NUS Repository. https://doi.org/10.1109/IWFHR.2004.15
Abstract: It is crucial to use statistical language models (LMs) to improve the accuracy of Chinese offline script recognition. In this paper, we investigate the influence of several LMs on the contextual post-processing performance of Chinese script recognition. We first introduce seven LMs, i.e., three conventional LMs (character-based bigram, character-based trigram, word-based bigram), two class-based bigram LMs and two hybrid bigram LMs combining word-based bigrams and class-based bigrams. We then investigate how the LMs' perplexities are affected by training corpus size, smoothing methods and count cutoffs. Next, we demonstrate the above LMs' influence on the post-processing performance in terms of recognition accuracy, memory requirement and processing speed. Finally, we give a proposal to select a suitable LM in real recognition tasks. © 2004 IEEE.
Source Title: Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR
URI: http://scholarbank.nus.edu.sg/handle/10635/40765
ISBN: 0769521878
ISSN: 15505235
DOI: 10.1109/IWFHR.2004.15
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