Deep Neural Network Training and Testing Datasets for License Plate Recognition

Abstract

Modern society has made tremendous progress towards automation to increase the quality of life and reduce the margin of human error. Intelligent transportation systems are a critical aspect of this evolution. The core technology of these systems is the automatic identification of vehicles' license plates to monitor safety and control violations of traffic rules and other crimes. The research on license plate detection and recognition has gone a long way, from traditional computer vision techniques to features (color, shape, text, etc.) based classification and finally to modern deep learning structures. The deep networks comprising hundreds of layers require enormous amounts of training data. The training dataset should contain plates from different countries; otherwise, the system will be specific to only certain types of plates (from a country or province). There are several datasets collected by researchers containing large numbers of license plates from different countries. This paper provides a detailed survey of such datasets available in the public domain. Sample images from each dataset are shown, and details such as the dataset size, size of images, download link, and country of origin are provided. This survey will be a helpful reference for new researchers in the field for the tasks of training new networks and benchmarking their performances.