Abstract
Handwritten Uyghur character recognition researches up to date have been based on traditional pattern recognition techniques that highly relies on handcrafted features. The similarity between character forms has been hindering the extraction of robust features. This paper proposed the deep learning based self-learned features to recognize 128 handwritten Uyghur characters forms. The first-hand online handwritten trajectory is first preprocessed and converted to a centralized binary image as input to the implemented deep neural network model. In experiments, the convolutional neural network models with 4, 5 and 8 convolutional layers are studied to get higher recognition accuracy. All models are trained implementing the same dropout regularization. The models 8 convolutional layers on 48 * 48 converted character images produced as high as 94.65% average accuracy on a test set of 10,240 handwritten character samples.
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Acknowledgment
This work is supported by National Key Research and Development Plan of China (2017YFC0820603), Natural Science Youth Foundation of Universities in Autonomous Region (No. XJEDU2019Y007) and Natural Science Foundation of Xinjiang (No. 2020D01C045). The first author is very much grateful to the National Laboratory of Pattern Recognition of CASIA for providing the experimental environment.
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Simayi, W., Ibrayim, M., Hamdulla, A. (2021). Handwritten Uyghur Character Recognition Using Convolutional Neural Networks. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_49
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DOI: https://doi.org/10.1007/978-3-030-72795-6_49
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