Ankush Roy, Debarshi Patanjali Ghoshal Department of Elec. Engg . Jadavpur University

1. Number plate recognition for use in different countries using an improved segmentation Ankush Roy, DebarshiPatanjaliGhoshalDepartment of Elec. Engg.Jadavpur University NCETACS 2011

2. Why ANPR ? ANPR – Automatic Number Plate Recognition Transborder Traffic Control Authorities Road Security (Check on notorious Drivers in black list) Car Log in Parking areas Primarily developed to cater to the needs of the law enforcement agencies. An important figure worth mentioning in this regard is that Britain itself has 10,502 ANPR and most of their locations are kept secret. Thames Valley police, which has released details of spending but not locations, has put nearly £2m into 47 fixed cameras, 31 in road vehicles, 11 portable kits and one in a helicopter. Data courtesy Guardian.co.uk Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

3. The Architecture Software part Input image Segmentation Unit Pre-processor Recognizer Analyzer Percentage Accuracy Output The approach do handles the entire ANPR module addressing each of the steps but the novelty lies in the segmentation scheme adopted Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

4. Pre-processing Image denoising Adaptive Thresholding A statistical Median filter is used to remove salt and pepper noise from the image in gray scale before binarizing. we have used a 3 × 3 masking sub window for this purpose. Both Otsu method and Ni back’s method were tested. Otsu method was finally used as it is globally adaptive which would increase processing speed as compared to Niback’s threshold scheme. Without Filtration After Filtration Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

5. Segmentation MOTIVATION Alphanumeric characters of the License Plate are the ones that have the higher pixel count among the pixel clusters Clustering of white pixel zones Component labeling of the clusters Sorting the component clusters Directional region growing of the clusters Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

6. Clustering of white pixel zones Clustering The clustering of the pixels are done on the basis of an eight connected neighborhood of the white pixels. Since wiener filtration was used previously so Impulse Noise was largely eliminated, hence Algorithm works more on relevant data having less noise Brings down Processing time Test Image from Jerome Coninx database Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

7. Component labeling of the clusters Each white cluster is labeled with a particular component tag Component tag : Algorithm scans the entire image and assigns a number to each cluster that it faces. The number is initialised by 0 and incremented by one when it jumps to the next cluster Number of pixels in each pixel cluster is recorded against the component tag and the position of each cluster (corner co-ordinates) are noted Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

8. Sorting the component clusters Pixel count in each cluster is then sorted in a descending manner Number of characters (n) specified by the Law Enforcement Agencies is taken as the input and a buffer of (2n-2) is set The value of (2n-2) is determined empirically to cope up with the over segmented characters Graph showing number of pixels in each cluster against the order in which they appear after sorting Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

9. Directional region growing of the clusters A problem still persists that many over segmented characters that have entered the calculation because of the buffer value (2n-2) set. Now the need is to associate these glyphs into relevant characters What we presently have A sorted matrix of the pixel clusters which has (2n-2) number of members A matrix containing the positional information of the clusters Directional Region Growing is used based on the observation that distance within glyphs of the same character is less than that within glyphs of different characters Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

10. Directional region growing of the clusters Using the positional information (x-axis) check the dist between the rightmost pixel of a cluster and the leftmost pixel of the cluster next to it. Ifdist<dcritical • Use this pixel as seed , join the region between the two pixel • horizontally • Dilation of the joined line • Re-label the entire image using 8-connected neighbourhood • Sort the pixel count and check the condition again T in the upper row is approximated by horizontal Region growing and 7 by Vertical region growing The entire algorithm is repeated again using (y-axis) distance check and comparing distances between lower most point of upper cluster and uppermost point of lower cluster . The process stops when minimization of the number of characters is not further possible Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

11. Segmentation and Normalization Segmentation The individual characters are then segmented using bounding box Now the glyphs do vary Greatly in shape so …. Normalization This normalization is done on the basis of size of the extracted images. All of them are scaled to [15x15] pixels Segmented and normalized arranged according to positional information Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

12. Recognition Module • Automatic Neural Network (ANN) based recognition scheme The activation function ( slope parameter in the sigmoid function is set to 1) Weight update function (α is the learning term β is the momentum Parameter E is the error term) • It consisted of 225 input nodes • 36 output nodes (26 uppercase letters and the 10 digits) • 1 hidden layer with 300 neurons Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

13. Database and train set The database comprised of 150 different images of license plates used in 58 different countries of the world. Entire Test Dataset Available at www.ankushroy.webs.com 75 images were used for training and the rest used as test set The individual pixel values were used as the input of the 15x15 binary image of individual characters segmented Here the module has the option of allowing the end user to select the appropriate Images (75). Just name the countries and the network selects them from the pool of images Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

14. Experimentation and error analysis The percentage accuracy here is based on the character wise reconstruction of the license plate after passing through the Recognizer. Calculated over the entire set a accuracy of 91.59 % was reached The skewness of the number-plate and improper lighting condition in many cases are the main limiting factors that affect the recognition percentage adversely Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

15. References and Acknowledgement Prof . Anjan Kr. Rakshit, Department of Elect Engg. Jadavpur University, Kolkata, [1] Vehicle Registration Plates of India. Available: http://en.wikipedia.org/wiki/Vehicle_registration_plates_of_India [2] WardNicholson, “License Plate Fonts of the Western World”, Available:http://www.leewardpro.com/articles/licplatefonts/licplate-fonts-intro.html [3] Parking and Traffic Technologies Ltd, Smartreg ANPR, Available:http://www.parkingandtraffic.co.uk/ANPR/smartreg-anpr [4] J.A.G. Nijhuis, M.H ter Brugge, and K.A. Helmolt, “Car License Plate recognition with network and fuzzy logic”, in Proc. Of IEEE International Conference on Neural Networks., volume 5, pp 2232-2236, Dec 1995 [5] Shyang-Lih Chang, Li Shein Chen, Yun-Chung Chung, and Sei-Wan Chen, “ Automatic license plate recognition” IEEE Transaction Intelligent Transportation System, 5:42-53,2004 Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University

16. Thank You Any Questions?? Ankush Roy & Debarshi Patanjali Ghoshal Jadavpur University