Learning algorithm for color recognition of license plates

Abstract

To improve accuracy and adaptability, this paper presents a learning algorithm for color recognition of license plates. For three components of the hue-saturation-value (HSV) color space, different membership functions were defined to calculate their fuzzy degrees. Through the weighted fusion of the three membership degrees, a single map was produced to be the classification function for color recognition, and the final decision is based on the integrated map. Thresholds of membership functions, weight vectors of membership degrees and classification thresholds were all learned by the proposed learning algorithm, according to the classification error minimization inductive principle. Experiments were conducted on two different test sets. The overall accuracies of the proposed algorithm are 97.70% and 96.20%, respectively. The experimental results show that the proposed algorithm can learn the appropriate thresholds and weights from the training images, which are consistent with the practical application environments. Thus it improves the accuracy and adaptability of the color recognition algorithm and can meet the requirements of the practical engineering applications.