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An end-to-end network for irregular printed Mongolian recognition

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Abstract

Mongolian is a language spoken in Inner Mongolia, China. In the recognition process, due to the shooting angle and other reasons, the image and text will be deformed, which will cause certain difficulties in recognition. This paper propose a triplet attention Mogrifier network (TAMN) for print Mongolian text recognition. The network uses a spatial transformation network to correct deformed Mongolian images. It uses gated recurrent convolution layers (GRCL) combine with triplet attention module to extract image features for the corrected images. The Mogrifier long short-term memory (LSTM) network gets the context sequence information in the feature and finally uses the decoder’s LSTM attention to get the prediction result. Experimental results show the spatial transformation network can effectively recognize deformed Mongolian images, and the recognition accuracy can reach 90.30%. This network achieves good performance in Mongolian text recognition compare with the current mainstream text recognition network. The dataset has been publicly available at https://github.com/ShaoDonCui/Mongolian-recognition.

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Notes

  1. China Mongolian News Network—http://www.mgyxw.net.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61966027 and 61966028), Inner Mongolia Autonomous Region Science and Technology Plan (Grant Nos. 2021GG0329 and 2021GG0140), National Natural Science Foundation of Inner Mongolia (Grant No. 2021MS06028). The authors wish to thank all the editors and anonymous reviewers for their constructive advice.

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Authors and Affiliations

  1. School of Information Engineering, Inner Mongolia University of Technology, Huhhot, China

    ShaoDong Cui, YiLa Su, Ren Qing dao er ji & YaTu Ji

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  1. ShaoDong Cui
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  2. YiLa Su
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  4. YaTu Ji
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Correspondence to YiLa Su.

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Cui, S., Su, Y., Qing dao er ji, R. et al. An end-to-end network for irregular printed Mongolian recognition. IJDAR 25, 41–50 (2022). https://doi.org/10.1007/s10032-021-00388-y

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