Considering the characteristic of Mongolian words where all letters of one Mongolian word are conglutinated together, the segmentation-free strategy is more suitable for Mongolian word recognition. This paper presents a novel recognition method based on MWRCNN and position maps for online handwritten Mongolian word. Firstly, the incorporation of position maps and aspect ratio is used to construct data transformation layer and enrich the Mongolian word shape information. Secondly, two feature combination methods based on MWRCNN are proposed to improve the recognition accuracy. Thirdly, by adopting multiple classification combination strategy, the accuracy of OHMWR can be further improved. We evaluated the recognition performance on online handwritten Mongolian word database with 946 classes. Experimental results show the proposed methods achieved the word-level recognition rate of 92.22% with data transformation, 92.60% with multiple feature combination and 93.24% with multiple classifier combination, respectively, which are better than the benchmarking test result 91.20% reported in the literature.
['Ji Liu', 'Longlong Ma', 'Jian Wu']
Online Handwritten Mongolian Word Recognition Using MWRCNN and Position Maps