Vertical Edge Detection for Car License Plate Recognition
There are various methods for recognizing the license plate of cars. In this project, a novel method for car-license plate recognition is used and presents different contributions. The number plate is initially detected precisely on the basis of edge detection algorithms. Next the detected number plate image is fed as input to the recognition module. The first step is that the input detected image is binarized using thresholding technique. It is one among the simplest segmentation processes. In the second step feature extraction is performed on the thresholded output. The objective of feature extraction is to capture the essential characteristics of the characters obtained. Feature extraction is performed based on the parameters such as euler number, major axis length, minor axis length, row and column features, convex area, orientation, filled area etc. Next the feature extracted output is trained using appropriate neural networks .Finally the trained outputs are classified and the exact number is displayed in the user interface platform. The advantage of our method is that even very low resolution images taken by a camera can be processed. Different license plate images under different conditions are given as input and the results are checked. The results show more or less accurate performance and faster processing time.
Optical character recognition, Adaptive thresholding, Neural networks, Feature extraction, Segmentation.