Abstract
There are many connected characters in cursive Uyghur handwriting, which makes the segmentation and recognition of Uyghur words very difficult. To enable large vocabulary Uyghur word recognition using character models, we propose a character separation method for over-segmentation in online cursive Uyghur handwriting. After removing delayed strokes from the handwritten words, potential breakpoints are detected from concavities and ligatures by temporal and shape analysis of the stroke trajectory. Our preliminary experiments on an online Uyghur word dataset demonstrate that the proposed method can give a high recall rate of segmentation point detection.
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Ibrahim, M., Zhang, H., Liu, CL., Hamdulla, A. (2012). An Effective Character Separation Method for Online Cursive Uyghur Handwriting. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_65
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DOI: https://doi.org/10.1007/978-3-642-33506-8_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
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