Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30819
Title: A hybrid post-processing system for offline Handwritten Chinese Character Recognition based on a statistical language model
Authors: Xu, R
Yeung, DS
Shi, D
Keywords: Distant word BI-gram model
Neural networks classifier
Offline handwritten Chinese character recognition
Post-processing
Issue Date: 2005
Publisher: World Scientific
Source: International journal of pattern recognition and artificial intelligence, 2005, v. 19, no. 3, p. 415-428 How to cite?
Journal: International journal of pattern recognition and artificial intelligence 
Abstract: This paper presents a post-processing system for improving the recognition rate of a Handwritten Chinese Character Recognition (HCCR) device. This three-stage hybrid post-processing system reduces the misclassification and rejection rates common in the single character recognition phase. The proposed system is novel in two respects: first, it reduces the misclassification rate by applying a dictionary-look-up strategy that bind the candidate characters into a word-lattice and appends the linguistic-prone characters into the candidate set; second, it identifies promising sentences by employing a distant Chinese word BI-Gram model with a maximum distance of three to select plausible words from the word-lattice. These sentences are then output as the upgraded result-Compared with one of our previous works in single Chinese character recognition, the proposed system improves absolute recognition rates by 12%.
URI: http://hdl.handle.net/10397/30819
ISSN: 0218-0014
EISSN: 1793-6381
DOI: 10.1142/S0218001405004046
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