1. An efficient method of license plate location Pattern Recognition Letters 26 (2005) 2431-2438 Journal of Electronic Imaging 11(4), 507-516 (October 2002) Presented by - Waseem Khatri

2. Objective : To efficiently locate a license plate in an image Motivation: License plate recognition can be an essential tool for Road traffic monitoring Automatic payments of tolls on highways & bridges Parking lot access control Ticketing speeding vehicles

3. Algorithm Image Enhancement Vertical Edge Extraction Noise Removal Plate Location License plate Extraction from Original image

4. Edge Information Plate area contains rich edge information Background areas around the plate mainly include horizontal edges Background areas have long curves and random noises If only the vertical edges are extracted from the car image and most of the background is removed, the plate area can be isolated

5. Image Enhancement The input image is converted to a gray scale image of size 384 X 288 Gradients in the image due to improper lighting conditions Few vertical edges in the plate area Enhancement is necessary Calculate the luminance and variance of each pixel Bilinear Interpolation 8 X 8 Blocks Enhancement Coefficient

6. Edge Extraction and Noise Removal Vertical edge extraction using Sobel Operator Thresholding Background curve and noise removal is done using the Concerned Neighborhood Pixel (CNP) Algorithm CNP checks all pixels around the concerned pixel and decides if it’s a randam noise pixel or a genuine edge pixel

7. License plate search A window of size (H X W) is passed through the CNP output image Total number of edge points in the window are counted Candidates are selected if they are above a certain threshold Maximum value among the candidates is considered as a final result The co-ordinates are noted and the plate is extracted from the orignal image

8. Results:

10. System Application Image Enhancement BLI Vertical Edge Extraction Noise Removal CNP Plate Location Affine Transformation Character Extraction (Segmentation) Classifier Output System Hotelling Transform Blob Coloring BayesianFisherNeural Nets

11. Conclusion Advantages Higher recognition rate compared to other methods like Line sensitive filters (Luis et al., 1999), Row-wise & Column-wise DFT’s (Parisi et al., 1998), Edge image improvement method (Ming et al., 1996) Drawbacks Slower than other four methods Calculated values of luminance and variance using Bilinear Interpolation are not actual values Image size is fixed 288 X 384 Window size of the license plate search is fixed Bilinear Interpolation is the most computationally intensive procedure in the given algorithm