Performance Analysis of Vehicle Number Plate Recognition System Using Template Matching Techniques

Vehicle number plate recognition (VNPR) system is a digital image processing techniques which is broadly used in vehicle transportation system to identify the vehicle by their number plate. Yet it’s a very challenging problem, due to the diversity of plate formats, different scales, and non-uniform illumination conditions during image acquisition. This research mainly focuses on Nepali vehicle number plate recognition system in which the vehicle plate image is received by the digital cameras and the image was then processed to obtain the number plate information. A real image of a vehicle is captured and processed using various algorithms. Morphological operations, and edge detection, smoothing, filtering, techniques for plate localization and characters segmentation for segment character and these segmented character was cut in to block of 70×70 size and calculate the correlation with the template of database using the template matching algorithm normalized cross correlation and phase correlation and compare this result in term of accuracy. The system was tested by 90 patterns under several conditions. It includes experiment of number plate recognition using phase correlation and normalized cross correlation methods. From the study and analysis of test after applying on number of images of database, the normalized cross correlation method was found more accurate to recognize the number plate then phase correlation method and recognition accuracy of normalized cross correlation was 67.98% and phase correlation was 63.46%.