Automatic car number plate recognition

A traffic surveillance system includes detection of vehicles which involves the detection and identification of license plate numbers. This paper proposes an intelligent approach of detecting vehicular number plates automatically using three efficient algorithms namely Ant colony optimization (ACO) used in plate localization for identifying the edges, a character segmentation and extraction algorithm and a hierarchical combined classification method based on inductive learning and SVM for individual character recognition. Initially the performance of the Ant Colony Optimization algorithm is compared with the existing algorithms for edge detection namely Canny, Prewitt, Roberts, Mexican Hat and Sobel operators. The Ant Colony Optimization used in communication systems has certain limitations when used in edge detection like random initial ant position in the image and the heuristic information being highly dictated by transition probabilities. In this paper, modifications like assigning a well-defined initial ant position and making use of weights to calculate heuristic value which will provide additional information about transition probabilities are used to overcome the limitations. Further a character extraction and segmentation algorithm which uses the concept of Kohonen neural network to identify the position and dimensions of characters is presented along with a comparison with the existing Histogram and Connected Pixels approach. Finally an inductive learning based classification method is compared with the Support Vector Machine based classification method and a combined classification method which uses both inductive learning and Support Vector Machine based approach for character recognition is proposed. The proposed character recognition algorithm may be more efficient than the other two.