creator Wazalwar, Dhawal S. advisor Oruklu, Erdal 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. Submitted by Dana Lamparello (dlampare@iit.edu) on 2012-03-14T21:38:05Z No. of bitstreams: 1 Licenseplaterecognitionincomplexscenes_finaldoc_cd.pdf: 1091717 bytes, checksum: 7dfdfa4e8cf4f4a53b21f4d878c97fa5 (MD5) Made available in DSpace on 2012-03-14T21:38:05Z (GMT). No. of bitstreams: 1 Licenseplaterecognitionincomplexscenes_finaldoc_cd.pdf: 1091717 bytes, checksum: 7dfdfa4e8cf4f4a53b21f4d878c97fa5 (MD5) Previous issue date: 2011-12 M.S. in Electrical Engineering, December 2011 2011-12-07 2011-12 http://hdl.handle.net/10560/2634 en Thesis born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access ECE / Electrical and Computer Engineering Illinois Institute of Technology Affiliated department