Representation learning in a deep network for license plate recognition

Abstract

The goal of license plate recognition (LPR) is to read the license plate characters. Due to image degradation, there are many difficulties in the way of achieving this goal. In this paper, the proposed method recognizes the license plate characters without employing the traditional segmentation and binarization techniques. This method uses a deep learning algorithm and tries to achieve better learning experience by engaging a multi-task learning algorithm based on sharing features. The features of license plate characters are extracted by a deep encoder-decoder network, and transferred to 8 parallel classifiers for recognition. To evaluate the current work, a database of 11,000 license plate images, collected from a currently working surveillance system installed on a dual carriageway, is employed. The proposed method achieved the correct character recognition rate of 96% for 4000 test images that is acceptable in comparison to the competing methods.