Weakly Supervised Deep Learning for Vehicle License Plate Recognition

Document Type : Research Paper


School of Computer Engineering, Iran University o f Science and Technology, Tehran, Iran


Deep learning, if used by an appropriately large dataset, can result in considerable accuracy. However, preparing a large dataset with accurate annotation is a costly process. For this reason, weakly supervised learning algorithms have attracted many researchers in recent years. In a weakly supervised learning approach, a large dataset is gathered with weak annotation to meet both the need of large dataset for deep learning, and reduce the cost of annotations. In this paper, a weakly supervised learning algorithm is proposed to recognize vehicle license plates using deep convolutional neural networks. In the training phase, only the characters existed in the plate images are labeled, and the proposed algorithm can recognize the presence of each character in addition to their coordinates. Therefore, in the test phase, the license plate is fully recognizable. In order to evaluate the performance of the proposed algorithm, a dataset containing 1397 plate images has been collected. The results of the experiments show that 95.6% of the plates and 99.1% of the characters are correctly recognized.