Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks
This addresses the problem of efficient and accurate license plate recognition for applications like traffic monitoring, though it is incremental as it builds on existing deep learning methods.
The paper tackles car license plate detection and recognition in natural scenes by proposing a unified deep neural network that localizes plates and recognizes letters in a single forward pass, trained end-to-end. Experiments on three datasets show it avoids intermediate error accumulation and accelerates processing speed.
In this work, we tackle the problem of car license plate detection and recognition in natural scene images. We propose a unified deep neural network which can localize license plates and recognize the letters simultaneously in a single forward pass. The whole network can be trained end-to-end. In contrast to existing approaches which take license plate detection and recognition as two separate tasks and settle them step by step, our method jointly solves these two tasks by a single network. It not only avoids intermediate error accumulation, but also accelerates the processing speed. For performance evaluation, three datasets including images captured from various scenes under different conditions are tested. Extensive experiments show the effectiveness and efficiency of our proposed approach.