Plate number character vote algorithm based on SVM (support vector machine) confidence
A confidence and character technology, applied in the field of computer vision, can solve problems such as wrong voting results, achieve the effect of improving recognition accuracy, reducing the number of target categories, and ensuring accuracy
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[0015] (1) Use the adboost algorithm based on haar features for license plate detection. If a license plate is detected, turn on voting and set no_plate_cnt=0. If no license plate is detected, then no_plate_cnt++, no_plate_cnt reaches a certain threshold or the number of votes reaches a certain threshold. Consider Vehicles leave, voting stopped.
[0016] (2) Use SVM for character recognition and output confidence. Since license plates in mainland China include Chinese characters, letters, and numbers, in order to improve the recognition accuracy, three classifiers were trained, namely Chinese character classifiers, letter classifiers, and alphanumeric classifiers. Multi-class classification, using multiple two-class voting methods, calculates the distance between the current feature vector and the support vector according to the support vectors of the two classes. The distance reflects the confidence of character recognition. If the distance is small, the confidence is high, a...
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