Abstract | ||
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This paper proposes a knowledge-based system to recognize historical Mongolian documents in which the words exhibit remarkable variation and character overlapping. According to the characteristics of Mongolian word formation, the system combines a holistic scheme and a segmentation-based scheme for word recognition. Several types of words and isolated suffixes that cannot be segmented into glyph-units or do not require segmentation are recognized using the holistic scheme. The remaining words are recognized using the segmentation-based scheme, which is the focus of this paper. We exploit the knowledge of the glyph characteristics to segment words into glyph-units in the segmentation-based scheme. Convolutional neural networks are employed not only for word recognition in the holistic scheme, but also for glyph-unit recognition in the segmentation-based scheme. Based on the analysis of recognition errors in the segmentation-based scheme, the system is enhanced by integrating three strategies into glyph-unit recognition. These strategies involve incorporating baseline information, glyph-unit grouping, and recognizing under-segmented and over-segmented fragments. The proposed system achieves 80.86 % word accuracy on the Mongolian Kanjur test samples. |
Year | DOI | Venue |
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2016 | 10.1007/s10032-016-0267-1 | IJDAR |
Keywords | Field | DocType |
Historical Mongolian document, Holistic recognition, Segmentation-based recognition, Convolutional neural network, Knowledge-based strategy, Optical character recognition | Glyph,Word formation,Intelligent character recognition,Pattern recognition,Segmentation,Convolutional neural network,Computer science,Word recognition,Optical character recognition,Artificial intelligence,Intelligent word recognition | Journal |
Volume | Issue | ISSN |
19 | 3 | 1433-2825 |
Citations | PageRank | References |
3 | 0.50 | 19 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
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Xiangdong Su | 1 | 18 | 4.68 |
Guanglai Gao | 2 | 78 | 24.57 |
Hongxi Wei | 3 | 35 | 5.71 |
Fei Long | 4 | 16 | 13.09 |