Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55734
Title: A hybrid post-processing system for handwritten chinese character recognition
Authors: Xu, RF
Yeung, DS
Shu, WH
Liu, JF
Keywords: Confusing character set
Dictionary-based approximate matching
Handwritten Chinese character recognition
Post-processing
Word Bi-gram model
Issue Date: 2002
Publisher: World Scientific
Source: International journal of pattern recognition and artificial intelligence, 2002, v. 16 , no. 6, p. 657-679 How to cite?
Journal: International journal of pattern recognition and artificial intelligence 
Abstract: In this paper, a hybrid post-processing system for improving the performance of Handwritten Chinese Character Recognition is presented. In order to remove two kinds of frequently encountered errors in the recognition result, namely mis-recognized character and unrecognized character, both confusing character characteristics of the recognizer and the contextual linguistic information are utilized in our hybrid three-stage post-processing system. In the first stage, the confusing character set and a statistical Noisy-Channel model are employed to identify the most promising candidate character and append possible unrecognized similar-shaped characters into candidate character set when a candidate sequence is given. Secondly, dictionary-based approximate word matching is conducted to further append contextual linguistic-prone characters into candidate character set and bind the candidate characters into a word-lattice. Finally, a Chinese word Bi-Gram Markov model is employed in the third stage to identify a most promising sentence by selecting plausible words from the word-lattice. On the average, our system achieves a 5.1% recognition rate improvement for the first candidate when the original character recognition rate is 90% for the first candidate and 95% for the top-10 candidates by an online HCCR engine.
URI: http://hdl.handle.net/10397/55734
ISSN: 0218-0014
EISSN: 1793-6381
DOI: 10.1142/S0218001402001964
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