A New GVF Arrow Pattern for Character Segmentation from Double Line License Plate Images

Abstract

License plate recognition is a live problem for several developing countries because of its many challenges. One of such challenges is character segmentation from double lines (alphabets in one line and numerals on another line) license plate images, where we can see touching between adjacent characters (horizontally) and lines (vertically). This is the major cause for poor recognition performance. Therefore, we propose a novel technique based on Gradient Vector Flow (GVF) to segment characters from double line license plate images. The proposed technique explores a new GVF arrow pattern, which represents spaces between lines and characters based on the fact that the force in concavity created between characters and lines according to the fact that curved shaped characters attract GVF arrows in unique fashion. This observation leads to find seed space patches for segmentation. The spatial coordinates of seed space patches are passed through Hough transform to find line separators. Next, the proposed technique searches for seed space patches, which are perpendicular to line separators to find character separators. Experimental results on double line license plate images show that the proposed technique is robust to touching, rotations, scaling, distortion, and outperforms the existing character segmentation methods. The recognition experiments before and after segmentation show that the proposed segmentation is significant in improving license plate recognition rate.