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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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Submitted on : Thursday, December 14, 2006 - 6:07:22 PM
Last modification on : Friday, October 8, 2021 - 4:26:19 PM
Long-term archiving on: : Thursday, September 20, 2012 - 4:06:40 PM


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  • HAL Id : inria-00120408, version 1
  • PRODINRA : 252101



Cheng-Lin Liu. Handwritten Chinese character recognition: Effects of shape normalization and feature extraction. Arabic and Chinese Handwriting Recognition (SACH06), Sep 2006, Maryland / USA, United States. ⟨inria-00120408⟩



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