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| Pak-Kwong Wong, Chorkin Chan, "Off-Line Handwritten Chinese Character Recognition as a Compound Bayes Decision Problem," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 9, pp. 1016-1023, September, 1998. | |||
| BibTex | x | ||
| @article{ 10.1109/34.713366, author = {Pak-Kwong Wong and Chorkin Chan}, title = {Off-Line Handwritten Chinese Character Recognition as a Compound Bayes Decision Problem}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {20}, number = {9}, issn = {0162-8828}, year = {1998}, pages = {1016-1023}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.713366}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Off-Line Handwritten Chinese Character Recognition as a Compound Bayes Decision Problem IS - 9 SN - 0162-8828 SP1016 EP1023 EPD - 1016-1023 A1 - Pak-Kwong Wong, A1 - Chorkin Chan, PY - 1998 KW - Off-line handwritten Chinese character recognition KW - Chinese language modeling KW - compound Bayes decision KW - contextual vector quantization KW - Chinese word segmentation. VL - 20 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—A handwritten Chinese character off-line recognizer based on Contextual Vector Quantization (CVQ) of every pixel of an unknown character image has been constructed. Each template character is represented by a codebook. When an unknown image is matched against a template character, each pixel of the image is quantized according to the associated codebook by considering not just the feature vector observed at each pixel, but those observed at its neighbors and their quantizations as well. Structural information such as stroke counts observed at each pixel are captured to form a cellular feature vector. Supporting a vocabulary of 4,616 simplified Chinese characters and alphanumeric and punctuation symbols, the writer-independent recognizer has an average recognition rate of 77.2 percent. Three statistical language models for postprocessing have been studied for their effectiveness in upgrading the recognition rate of the system. Among them, the CVQ-based language model is the most effective one upgrading the recognition rate by 10.4 percent on the average.
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