Mine Loadometer License Plate Recognition Based on MFF-DS

Abstract:

The result of license plate recognition with a single feature is unsatisfactory. A multi-feature fusion method based on D-S evidence theory is proposed to improve results of mine loadometer license plate recognition. Firstly, three kinds of features including contour, projection and trellis-coded are extracted from the vehicle plate character image. Then the Basic Probability Assignment (BPA) is defined to get the credibility of recognition results by using the multi-class Support Vector Machine (SVM) with one-against-one method. Finally, D-S evidence theory is employed to integrate the credibility of evidences for making a final decision. The experimental results show that the multi-feature fusion method has higher recognition rate, fault tolerance and robustness.

• Periodical: Advanced Materials Research (Volumes 712-715)
• Pages: 2341-2344
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.712-715.2341
• Online since: June 2013
• Authors: Xiu Cai Guo, Shi Qian Zhang
• Keywords: Character Recognition, D-S Evidence Theory, Mine Loadometer, Multi-Feature Fusion, Support Vector Machine (SVM)

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