Vehicle Tire Text Reader: Text Spotting and Rectifying for Small, Curved, and Rotated Characters

Abstract

Tire burst of vehicles is one of the main causes of highway accidents. Furthermore, tire aging is the most important cause of tire burst. It will be of great significance to traffic safety, through identifying the tire text code (TTC) to determine whether or not a tire is over the service life. In this article, a coarse-to-fine method of detection and rectification for TTC is proposed. First, text regions are located using the deep convolutional neural network (DCNN) model. Second, through fitting the tire circle curve, the close-up image of TTC is rotated to a positive direction. Third, a text boundary points’ generation strategy is proposed, which is based on the rotating bounding box of each character. Fourth, the curved text is rectified and input to the DCNN recognition model. On the tire image dataset, the detection accuracy of TTC and characters is 94% and 96.5% respectively, and the recognition accuracy is 94.5%. Through additional experimental results, it is proven that the proposed method has strong robustness to illumination and camera shooting angle, which can meet the real-time detection and recognition requirements as well.