One of the Character Recognition techniques is template matching. It’s miles the process of locating the region of a sub-photograph called a template, inside an picture. Template matching entails figuring out similarities between a given template and home windows of the same size in an image and figuring out the window that produces the very best similarity degree. it works by comparing each and every pixel of the photograph and template for every feasible template displacement. This method involves the use and help of a database of characters or templates. For all feasible input characters there exists a template. for every alphanumeric characters templates are created (from A-Z and zero-nine) the use of ‘regular’ font style. figure 8.2 shows the templates for few of the alphanumeric characters.For acknowledgment to take place, the present information character is contrasted with every format to discover either a feasible match, or the layout with the nearest portrayal of the information character. It can catch the ideal position where the character is by moving standard layout, in this manner do the correct match. Moving the layout coordinating technique depends on the format of the target character, utilizing the format of standard character to coordinate the objective character from eight bearings of up, down, left, right, upper left, bring down left, upper right, bring down right. The consequences of layout coordinating for character acknowledgment on a portion of the Indian number plates taken from static pictures are appeared in Table 1. The pictures of number Accuracy of 80.8% has been obtained. This accuracy can be advanced significantly by way of putting the digicam definitely to capture the perfect body and the use of two layers of neural networks. The implementation of the given system can be moved further for the popularity of quantity plates of multiple cars in a single photo body by way of the use of multi-level genetic algorithms.Additionally, a extra easier model of this gadget can be carried out by way of taking pictures from stationery video feed and selecting the great car frame for category of car types and spotting the quantity plates the use of neural networks.