Vehicle license plate recognition based on class-specific ERs and SaE-ELM

In this paper, an effective approach to vehicle license plate recognition based on Extremal Regions (ERs) and Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) is proposed. In the license plate detection step, some computations including morphological operations, various filters, different contours and validations are sequentially performed to extract some image regions as candidate license plates. Then, accurate character segmentation is achieved through a proper selection of ERs. In the character recognition step, the HOG (histogram of oriented gradients) feature vector in each character region is extracted, and then the characters are recognized using an offline trained pattern classifier of SaE-ELM. Experimental results show that our approach works quite well in complex traffic environments.