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It is common that historical Tibetan documents belonging to historical collections are poorly preserved and are prone to degradation processes. This causes many challenges that can be addressed by image binarization, the most common of which is stains. A lack of uniform standard datasets makes it difficult to evaluate binarization effects. Motivated by the poor effects and difficulty of evaluating the binarization of historical Tibetan document images, a combined approach is proposed that aims to improve overall performance. The method includes the following parts: first, image generation through standard binarization and the background extraction of color images, which are both used for image synthesis in preparing for an evaluation. Then, preliminary binarization processing is implemented through channel combination in the lab color space and through local binarization. The synthetic images are used to select the coefficient when the channels are combined. Furthermore, Local Binary Pattern (hereafter LBP) and image smoothing is carried out after the combination of the channels to obtain the outline of the text area. Finally, the final binarization image is obtained by combining the preliminary binarization image and the text area contour image. Our method achieved top performance compared to other methods after a large number of synthetic image tests with a variety of background types. [ABSTRACT FROM AUTHOR]

The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is 7 2 4 0 × 5 0 0 0. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance. [ABSTRACT FROM AUTHOR]