License Plate Recognition in Complex Scenes
License plate recognition is considered to be one of the fastest growing tech- nologies in the field of surveillance and control. In this project, we present a new design flow for robust license plate localization and recognition. The algorithm con- sists of three stages: i) license plate localization ii) character segmentation and iii) feature extraction and character recognition. The algorithm uses Mexican hat opera- tor for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artifi- cial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.