Abstract
This paper describes recent work on integrating multiple strategies to improve the performance of the Mongolian historical document recognition system which utilize the segmentation-based scheme. We analyze the reasons why the recognition errors happened. On such basis, we propose three strategies according to the knowledge of the glyph characteristics of Mongolian and integrate them into glyph-unit recognition. The strategies are recognizing the under-segmented and over-segmented fragments (RUOF), glyph-unit grouping (GG) and incorporating the baseline information (IBI). The first strategy helps in correcting the segmentation error and the remaining two strategies further improve the classifiers accuracies. The experiment on the historical Mongolian Kanjur demonstrates that utilizing these strategies could effectively increase the accuracy of word recognition.
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Acknowledgements
This work is funded by National Natural Science Foundation of China (Grant No. 61263037, No. 61463038, and No. 61563040) and the Research Project of Higher Education School of Inner Mongolia Autonomous Region of China (Grant No. NJZY14007).
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Su, X., Gao, G., Wei, H., Bao, F. (2015). Enhancing the Mongolian Historical Document Recognition System with Multiple Knowledge-Based Strategies. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_61
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DOI: https://doi.org/10.1007/978-3-319-26535-3_61
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