Paper Infomation
Research on the Gabor Feature Extraction Methods of Online Handwritten Tibetan Characters Recognition
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Author: Wenbin Lv, Weilan Wang, Xiaobao Lu
Abstract: Gabor filters have a good performance for texture feature extraction of texture images from a variety of low-quality handwritten character images. Therefore, this article proposes an online handwritten Tibetan character feature extraction methods which is based on the Gabor filter. The Gabor filter is used to process the Tibetan character image, block the image, extract the Gabor energy value of each block, and combine the energy values into the energy matrix, obtain the feature vector of the characters after feature dimension reduction, and then recognize the feature vector through the MQDF classifiers. The experiment shows that: the recognition of the samples in the data base of the handwritten Tibetan characters which is based on Gabor energy feature is simple and effective.
Keywords: Online handwritten Tibetan characters; Gabor filters; MQDF classifiers; Samples
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