An Analytical Approach for Enhancing the Automatic Detection and Recognition of Skewed Bangla License Plates

Abstract

Although there has been a huge body of work on Bangla license plate detection and recognition, the successes of these works have largely been limited to correct detection and recognition of undistorted license plates whose images are taken chiefly from the front or the back of vehicles with slight angular variations. As a result, most Bangla automatic license plate recognition (ALPR) systems in practice struggle when the license plates are skewed on the viewing or the image planes of the license plates. In this paper, we address this issue by proposing an analytical approach that can enhance the ALPR of both normal and skewed license plates and can be incorporated into existing Bangla ALPR systems without modifying their internal structures. Specifically, we demonstrate how existing ALPR systems can be treated as black boxes and analyzed to understand what sort of license plate images they work best on and introduce a novel pipeline that combines deep learning and an algorithmic procedure for transforming images of both normal and skewed license plates into formats that are best suited for the ALPR systems. We note that our proposed method can be easily generalized and applied to non-Bangla license plates as well.