Abstract
This paper proposes a recognition technique which applies a combination of image processing and pattern recognition to visual features of individual words. Uyghur script is naturally cursive, and its characters have uneven width. Therefore, in image format, precisely cutting Uyghur words into characters is difficult. To avoid such problem, we use word models instead of character models. Besides, this technique does not need a large amount of training samples: prepared text samples are converted to image samples which are used to construct individual word models.
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Meimaiti, H. (2018). A Study on the Printed Uyghur Script Recognition Technique Using Word Visual Features. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_76
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DOI: https://doi.org/10.1007/978-3-319-97909-0_76
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