A Cascade Framework for a Real-Time Statistical Plate Recognition System

This paper describes a plate recognition system that can process images rapidly at high accuracy rates. This system is designed to meet the requirements of performance, computational speed, and adaptation for vehicle surveillance applications, such as stolen car detection systems. These requirements are satisfied by adopting a cascade framework, utilizing plate characteristics, and developing fast one-pass algorithms. Our system is composed of three main cascading modules for plate detection, character segmentation, and postprocessing. Each module is further decomposed into several cascading procedures, which are composed of successively more complex rejecters. The first module rapidly rejects a majority of nonplate regions by using low computational gradient features and a one-pass scanning algorithm followed by heavy computational statistical rejecters. The second module rejects a majority of noncharacter regions in a similar manner. A peak-valley analysis algorithm is proposed to rapidly detect all promising candidates of character regions. The third module eliminates the plate characters that do not satisfy the plate specifications. In our experiments, the system can recognize plates over 38 frames per second with a resolution of 640 times 480 pixels on a 3-GHz Intel Pentium 4 personal computer