Assessment on Number Plate Detection and Recognition by Using Different Smoothing Algorithms

The technique known as “number plate detection” in image processing utilizes the license tag or plate number of a vehicle for identification purposes. The identification of car license plates holds significant importance in various domains, such as the armed forces, government organizations, as well as other relevant sectors. This research aims to examine the application of several smoothing filters, such as the Gaussian and Bilateral filters, and different color designs, which include HSV and YCBCR color designs, for the purpose of number plate identification. The initial step involves the conversion of the image into a YCBCR representation, which is subsequently transformed into a binary picture. This binary picture is then exposed to a series of morphological treatments. The altered image is further processed using a bilateral filter and contour detection technique in order to accurately identify the number plate. In an alternative study, we are doing an experiment wherein we eliminate the bilateral filter and substitute it with a Gaussian filter. We then proceed to examine the resultant effects on other analyses. In this investigation, we employ the HSV color model and implement the same methodology by utilizing both bilateral and Gaussian filters.