An Approach for License Plate Recognition Based on Temporal Redundancy

Recognition of vehicle license plates is an important task that can be applied for a myriad of real scenarios. Most approaches in the literature first detect an on-track vehicle, locate the license plate, perform a segmentation of its characters and then recognize them using an Optical Character Recognition (OCR) approach. However, these approaches focus on performing these tasks using only a single frame of each vehicle in the video. Therefore, such approaches might have their recognition rates reduced due to noise present in that particular frame. In this work we propose an approach to automatically detect the vehicle on the road and identify its license plate based on temporal redundant information. We also propose a postprocessing step that can be employed to improve the accuracy of the system. Experimental results demonstrate that it is possible to improve the vehicle recognition rate in 15.5 percentage points using our proposal temporal redundancy approach. Furthermore, additional 7.8 percentage points are achieved using the postprocessing technique, leading to a final recognition rate of 89.6%. Furthermore, this work also proposes a novel benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR, a new evaluation measure and an evaluation protocol. Keywords-automatic license plate recognition; pattern recognition; license plate character segmentation; benchmark