Affiliations: [a] College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China | [b] College of Computer Science and Technology, Xinjiang Normal University, Urumqi, Xinjiang 830054, China | [c] College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China | [d] The Base of Kazakh and Kirghiz Language of National Language Resource Monitoring and Research Center on Minority Languages, Urumqi, Xinjiang 830046, China | [e] Xinjiang Laboratory of Multi-language Information Technology, Urumqi, Xinjiang 830046, China
Correspondence:
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Corresponding author: Gulila Altenbek, College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China. E-mail: 47266861@qq.com.
Abstract: Taking Uyghur character recognition as an example, this paper use a method of image segmentation which combines traditional methods with CNN, and transforms and implements the Uyghur character on intelligent devices. Firstly, after analyzing the application of several general segmentation methods, this paper finds some shortcomings but also some ideas for Uighur segmentation. Then, starting from the characteristics of Uyghur language, such as structure, word formation and input habits, the author studies the idea of Uyghur adhesive language segmentation from the perspective of language characteristics, puts forward the basic algorithm of Uyghur symbol segmentation, and applies the Uyghur character adhesion segmentation based on minimum spanning tree and multi-queue primitive merging model to improve the segmentation efficiency. In addition, in order to solve the limitations of the traditional handwriting recognition framework of “preprocessing + feature extraction + classifier”, this paper puts forward a new solution of Uyghur handwriting recognition technology combining prior domain knowledge with CNN, constructs a Uyghur handwriting recognition model on the basis of CNN and random elastic deformation, and also improves the recognition rate of the system by combining domain knowledge with CNN. According to the experimental results, we can conclude that the mixed method proposed in this paper can effectively break the technical bottleneck of traditional methods, thus improving the efficiency of segmentation and recognition.
Keywords: Image segmentation method, prior domain knowledge, CNN, character recognition, deep learning
