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A Noise-Adaptive Discriminant Function and Its Application to Blurred Machine-Printed Kanji Recognition
March 2000 (vol. 22 no. 3)
pp. 314-319

Abstract—Accurate recognition of blurred images is a practical but previously to mostly overlooked problem. In this paper, we quantify the level of noise in blurred images and propose a new modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns.

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Index Terms:
Discriminant function, Mahalanobis distance, Bayes classifier, distribution of feature vectors, noise, blurred character recognition.
Citation:
Shin'ichiro Omachi, Fang Sun, Hirotomo Aso, "A Noise-Adaptive Discriminant Function and Its Application to Blurred Machine-Printed Kanji Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 3, pp. 314-319, March 2000, doi:10.1109/34.841761
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