Handwritten Chinese character recognition: Effects of shape normalization and feature extraction

Abstract : The ¯eld of handwritten Chinese character recog- nition (HCCR) has seen signi¯cant advances in the last two decades, owing to the e®ectiveness of many techniques, especially those for character shape normalization and feature extraction. This paper reviews the major methods of normalization and feature extraction, and evaluates their perfor- mance experimentally. The normalization meth- ods include linear normalization, nonlinear normal- ization (NLN) based on line density equalization, moment normalization (MN), bi-moment normaliza- tion (BMN), modi¯ed centroid-boundary alignment (MCBA), and their pseudo-two-dimensional (pseudo 2D) extensions. As to feature extraction, we fo- cus on some e®ective variations of direction features: chaincode feature, normalization-cooperated chain- code feature (NCCF), and gradient feature. We have compared the normalization methods previ- ously, but in this study, will compare them with better implementation of features. As results, the current methods perform superiorly on handprinted characters, but are insu±cient for unconstrained handwriting.
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Communication dans un congrès
Arabic and Chinese Handwriting Recognition (SACH06), Sep 2006, Maryland / USA, Springer, 2006, Lecture Notes in Computer Science
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Cheng-Lin Liu. Handwritten Chinese character recognition: Effects of shape normalization and feature extraction. Arabic and Chinese Handwriting Recognition (SACH06), Sep 2006, Maryland / USA, Springer, 2006, Lecture Notes in Computer Science. 〈inria-00120408〉

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