Traffic Sign Recognition (TSR) is now relatively well-handled by several approaches. However, traffic signs are often completed by one (or several) supplementary placed below. They are essential for correct interpretation of main sign, as they specify its applicability scope. difficulty of supplementary sub-sign recognition potentially infinite number of classes, as nearly any can be written on...

A system to learn and recognize traffic signs is described. The system uses neural network image processing and syntactic methods. The learning process is based on the representation of traffic signs by means of a grammar, which is inferred from a set of positive and negative samples. The recognition of traffic signs in a scene is done in two steps. First, the sign is located in the scene by us...

Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. Fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle or for...