Offline Handwritten Chinese Character Recognition Using Optimal Sampling Features

Authors:
Rui Zhang;Xiaoqing Ding
Affiliations:
-;-
Venue:
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Year:
2000

Citing 2
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A handwritten Chinese character recognition system using hierarchical displacement extraction based on directional features

Pattern Recognition Letters
Adaptive Normalization of Handwritten Characters Using Global/Local Affine Transformation

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

For offline handwritten Chinese character recognition, stroke variation is the most difficult problem to be solved. A new method of optimal sampling features is proposed to compensate for the stroke variations and decrease the within-class pattern variability. In this method, we propose the concept of sampling features based on directional features that axe widely used in offline Chinese character recognition. Optimal sampling features are then developed from sampling features by displacing the sampling positions under an optimal criterion. The algorithm for extracting optimal sampling features is proposed. The effectiveness of this method is widely tested using the Tsinghua University database (THCHR).