# License plate recognition in matlab (Statistical Pattern Recgnition)

Use suitable image for this assignment.

LPR Assignments

(1) Develop and demonstrate ROI code for the LPR images that locates one or more blocks on the plate, for 80% or more of the images. See Histogram-based Region of Interest (ROI) Function part of this document for some ideas.

(2) Develop and demonstrate plate segmentation (ROI followed by plate boundary location) code for the LPR images that outputs the complete plate successfully for 80% or more of the images.

(3) Develop and demonstrate character segmentation code that takes a plate image and outputs an individual image for each large character

(4) Develop and demonstrate a Matlab program that allows the user to click on plate characters and produce individual character images.

(5) Develop and demonstrate character recognition code that is able to recognize 80 % or more of large plate characters, given images generated as in part (4).

Histogram-based Region of Interest (ROI) Function :

The first part of Plate Finding is ROI. You could implement a method from the literature. Alternately, you can make up your own ROI method, as I’ve suggested below.

Before writing any code, determine a block size that roughly corresponds to ½ the height and ½ the width of a plate. The proposed algorithm could operate as follows:

(1) Calculate a separate histogram for each block, and delete the brightest 5% and the darkest 5% of the pixels. Based upon the brightest and darkest remaining grey levels, determine if the plate has good contrast, or if it does not. If it doesn’t, exit.

(2) Use the remaining parts of the histogram to determine a threshold for binarization. Binarize the block and median filter it.

(3) Scanning the block from left to right, find the number of similarly sized (in height) objects.

(4) If there are enough such objects, you’ve found a potential plate rather than a grill or a headlight.

Kĩ năng: Lập trình C++, Matlab and Mathematica

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