2.  Special thanks to our DIP Workshop mentors namely, Sunil Jaiswal, Manohar Kuse, Dushyant Goyal, Gaurav Mittal, Bhavya Agarwal.  Thanks also to Ayush Kumar & Nimisha Agarwal for their help in the learning stage.

3.  The software segregates the license plate out of the photo of a car taken from a camera.

4.  The motive was to find a white expanse of pixels which would be the license plate.

5.  We initially turned all other(except white) pixels to black  The initial points were noted and length was calculated,and if it turned out to be greater than a minimum amount,we assumed that this was the license plate.

6.  This method hugely failed for white cars.  It also failed for cars having reflections of lights.  It couldn’t differentiate between the headlight and the plate of the car.

7.  Due to difference in colors or layers between the plate and the chassis  Failure of previous method i.e Thresholding of the image to find the plate.

8.  Use of masks to identify all the edges of the plate.  The initial points were noted and length was calculated,and if it turned out to be greater than a minimum amount,we assumed that this was the license plate.  After the detection of plate,the output image will be produced.

9.  The predefined masks gave bad results.  Wrong detections of edges.  Broken edges of detected plate.  No result for slightly tilted plates.

10.  Synthesis of appropriate masks for good detection.We used four 5X5 masks all the sides of the plate.  Development of the method of “dependent padding” to overcome the problem of tilted plates and to enhance the detected edges.