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Multi-currency Notes Technology Research and Implementation

Author: ZhangHongWu
Tutor: LiuJiaFeng
School: Harbin Institute of Technology
Course: Computer Science and Technology
Keywords: Notes image recognition Feature extraction Defect detection Paper currency quality evaluation False notes recognition
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 90
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Notes image recognition is a popular topic in the field of pattern recognition in recent years, and it has a great future in the market. The paper currency classifier developed with this technique play a greater role in the bank role. The key technology of the classifier system is real-time image processing and image recognition. The classifier software processes the note image, and then sends the result of the paper currency to controlling system. The machine takes corresponding action to finish the classifying according to the answer. The classifier system has a high real-time requirement, it means that after notes pass the sensor, the system must output the notes information.On the basis of past achievement, we make several improvements according to the differences of different foreign paper currency:(1)According to different feature and real-time requirement of different notes, propose a new recognize method of combine the gridding feature and GMM, compare with the method of combine the gridding feature and distanced classifier, it is faster, and has a more high recognition rate.(2) According to different printing of banknotes of different patterns, a new method of discriminant old or new banknotes based on notes reflective of the light of the banknotes are proposed, supply a gap of determine the old or new banknotes based on a blank area of banknotes.(3)According to the demand of classify the incomplete-notes, an edge-based algorithm and a uniformity-based algorithm is proposed to detect the scratches and cracks appearing frequently on banknotes.(4) According to the demand of classify the false notes, a gray-based ratio algorithm and a gradient-based algorithm are proposed, finding the false banknotes effectively.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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