Degraded Character Recognition by Complementary Classifiers Combination | IEEE Conference Publication | IEEE Xplore

Degraded Character Recognition by Complementary Classifiers Combination


Abstract:

Character degradation is a big problem for machine printed character recognition. Two main reasons for degradation are extrinsic image degradation such as blurring and lo...Show More

Abstract:

Character degradation is a big problem for machine printed character recognition. Two main reasons for degradation are extrinsic image degradation such as blurring and low image dimension, and intrinsic degradation caused by font variations. A recognition method that combines two complementary classifiers is proposed in this paper. The local feature based classifier extracts the local contour direction changes, which is effective for character patterns with less structure deterioration. The global feature based classifier extracts the texture distribution of the character image, which is effective when the character structure is hard to discriminate. The two complementary classifiers are combined by candidate fusion in a coarse-to-fine style. Experiments are carried on degraded Chinese character recognition. The results prove the effectiveness of our method.
Date of Conference: 23-26 September 2007
Date Added to IEEE Xplore: 05 November 2007
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Conference Location: Curitiba, Brazil

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