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Character Independent Font Recognition on a Single Chinese Character

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3 Author(s)

A novel algorithm for font recognition on a single unknown Chinese character, independent of the identity of the character, is proposed in this paper. We employ a wavelet transform on the character image and extract wavelet features from the transformed image. After a Box-Cox transformation and LDA (linear discriminant analysis) process, the discriminating features for font recognition are extracted and classified through a MQDF (Modified quadric distance function) classifier with only one prototype for each font class. Our experiments show that our algorithm can achieve a recognition rate of 90.28 percent on a single unknown character and 99.01 percent if five characters are used for font recognition. Compared with existing methods, all of which are based on a text block, our method can provide a higher recognition rate and is more flexible and robust, since it is based on a single unknown character. Additionally, our method demonstrates that it is possible to extract subtle yet discriminative signals embedded in a much larger noisy background

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:29 ,  Issue: 2 )