Text detection and recognition on traffic panel in roadside imagery

Text recognition has revolutionized the world of image processing and intelligent transportation system (ITS). It opened several possibilities to traditional ITS concept. Advancement in text recognition has made it possible to implement text recognition in ITS. Traffic panel text recognition, a real time application is considered as a key addition to the revolution in modern ITS. This research aims at developing real time application for traffic panel text recognition for English and Thai. Text recognition in this paper is based on Support vector Machine (SVM), KNN and maximally stable extremal regions (MSER). Traffic panel are extracted based on visual appearance. Based on traffic panel background, color mask is applied to remove non text candidates. Multi-level MSER is used for segmentation. Raw pixel value of segmented character is used as feature vector. This system is trained for English and Thai language and then tested on roadside images of traffic panels. The results of different kernels are compared with each other to select best possible kernel for SVM. SVM results are then compared with KNN to find best classifier for this problem. The result shows that proposed system is robust and performs well in challenging environment.