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| A Handwriting Digital Recognition Method Based on Enhanced Objective Cluster Analysis |
| WANG Na1,2a, HU Chaofang2b
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1 a.Department of Automation, School of Electrical Engineering and Automation;
b.Tianjin Key Laboratory of Electrical and Electrical Technology, Tianjin Polytechnic University, Tianjin 300387, China;
2 a.Tianjin Key Laboratory of Micro Optical Electronic Mechanical System Technology, Ministry of Education;
b.Department of Automation, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China |
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Abstract The handwriting digital recognition methods generally are sensitive to the noise and the structure of image, which easily leads to the decrement of recognition accuracy and the increment of computation complexity. In this paper, the objective cluster analysis algorithm is introduced and combined with the template matching mechanism. In order to reduce the effects of the noise and the data distribution, and to improve the recognition accuracy, the one-pass clustering for the template set of the numeral to be identified is proposed. Furthermore, the new clustering centers are used to simplify the primary template dataset, by which the computation efficiency can be enhanced. The simulation about the random handwriting recognition in presence of structural deformation and noise demonstrates the simplicity, practicability and effectiveness of the proposed approach by comparing with the traditional methods
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Received: 15 April 2019
Published: 19 August 2019
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