Abstract
This paper proposes the offline Myanmar handwriting recognition system which uses the combination of deep learning approaches- convolutional neural networks to extract features from the input images and recurrent neural networks to recognize these features. To improve the performance of the system, some pre-processing techniques such as binarization, noise removing, image segmentation and resizing, are also applied before training the scanned images. The experiment is carried out on the own dataset related with office staff leave form documents since there is no free resources for Myanmar language. Performance of the proposed system is evaluated using both the character error rate (CER) and the word error rate (WER).
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Acknowledgements
The authors would like to give special thanks to the advisers from University of Information Technology who have given valuable ideas during the development of the proposed system and professors who have given advice and suggestion from the language point of view. Moreover, we would also like to thank the Rector and the administration department for allowing to use the data of office documents for the proposed system.
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Win, H.Y., Wai, T.T. (2020). Implementation of Myanmar Handwritten Recognition. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2019. Advances in Intelligent Systems and Computing, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-33585-4_32
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DOI: https://doi.org/10.1007/978-3-030-33585-4_32
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