A New Sharpness Based Approach For Character Segmentation In License Plate Images

Character segmentation from License plate is challenging as it suffers from non-uniform illumination, blur and touching adjacent character due to head light of vehicles effect etc. This paper presents a new approach based on sharpness of character and the space regions for segmentation. We explore Gradient Vector Flow Opposite Direction Pair of pixels for seed point's selection. From the seed points, the proposed approach detects probable cuts between character components based on minimum cost path estimation. Then we propose a new sharpness for detecting candidate cuts from the probable cuts based on Laplacian zero crossing points and Sobel-Gradient. The average width of candidate cuts is considered as reference cut to identify the correct cuts between character components. Experimental results on real industry dataset of license plate shows that the proposed approach is robust to touching, blur, poor quality images. Further, comparative study with an existing approach shows that the proposed approach outperforms the existing approach.