LICENSE PLATE RECOGNITION BASED ON TEMPORAL REDUNDANCY

Recognition of vehicle license plates is an important task in several real applications. Most approaches first detect a vehicle, locate the license plate and then recognize its characters. However, the focus relies 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. Instead of selecting a single frame to perform the recognition, we propose a novel real-time approach to automatically detect the vehicle and identify (locate/recognize) its license plate based on temporal redundancy information. To achieve further improvements, we also propose two post-processing techniques by querying a license plate database. The experimental results, performed in a dataset composed of 300 on-track vehicles acquired on an urban road, demonstrate that it is possible to improve the vehicle recognition rate in 15.3 percentage points using our proposal temporal redundancy approach. Additional 7.8 percentage points are achieved by querying registered license plates on a database by the vehicle appearance, leading to a final recognition rate of 89.6%. Furthermore, the technique is able to process 34 frames per second, which characterizes it as a real-time approach.