A comparison of methods for character recognition of car number plates

Number plate is the unique identification of a vehicle. Automatic Number Plate Recognition (ANPR) is a system to capture the number plate of a moving vehicle and then to recognize it. Real-time recognition of number plate of moving vehicles is critical in numerous applications such as surveillance of moving vehicles, tracking and stopping vehicles associated with known suspects. It can be used not only for security and traffic management purposes but also for safety and general information gathering applications. Aiming at general ANPR's objectives, namely fast processing speed, high accuracy and low cost, many researches focus on recognizing the characters on number plate image quickly and accurately. The goal of ANPR research is to develop algorithms and methods that can recognize number plate accurately and quickly at any one time, day or night, in almost any weather conditions. In this paper, the general process of image recognition system is extracted. This process includes preprocessing, feature extraction, classification, and optimization. Following this processing chain, each stage is discussed in detail and a few of well-known approaches used are represented. After analysis of the merits and pitfalls of existing systems, a combined recognition system is proposed for ANPR system. Using several eff icient classifiers such as Template Matching and Support Vector Machines (SVMs), the combined system can improve the processing ability not only in accuracy but also in speed.