Layout-invariant license plate detection and recognition

Abstract

Many current automatic license plate (LP) recognition systems are designed to handle a fixed form of LPs. In the present work, we develop an effective system using deep convolutional neuron network (CNN) that can process LPs with different layouts (e.g., variable character lengths, diverse colors, square-like and rectangular shapes). Firstly, we make an attempt of gathering a sufficient large and diverse Vietnamese LP dataset and manually creating the annotations for images. Secondly, a CNN model is derived to detect the LPs in images and predict the LP’s shape (i.e., one-row or two-row form). Thirdly, we design an efficient and unified CNN model to predict the characters of an input LP image patch. The proposed system has been extensively validated on two datasets (Vietnamese and Chinese LPs), demonstrating promising accuracy (e.g., 95% – 99%) and real-time CPU inference in comparison with the state-of-the-art approaches.