University of Tartu, Institute of Computer Science

Course: Introduction to Intelligent Transportation Systems Project: Automatic Plate Number Recognition (APNR) Student: Leonid Dashko

APNR: STEPS #1-5

1)

Load image

2)

Apply blur filter (to remove noise)

3)

Convert blurred image to grayscale

4)

Apply Sobel filter to find vertical edges (car plates have a high density of vertical lines)

5)

Apply threshold with Ostu’s Binarization

(Ostu’s binarization will automatically calculate optimal threshold from image histogram)

APNR: STEP #6 (Morphological filters in OpenCV)

6)

Then I created a rectangular mask of size of 17x3 and applied “closing” filter (shown on the right) to detect plate number more clearly

APNR: STEPS #7-9

7)

Find and fetch contours of possible plates

8)

Validate contours and clear out those, that can't be potential plate numbers - Is white color dominant?

- Rotated not more than 15 degrees - In Europe, car plate size: 52x11, aspect 4,7272 - Define min && max area of plate number 9)

After (8), apply “dilate” filter and threshold to validated contours to get numbers and characters

APNR: STEP #9 in details

APNR: STEP #10 (parse plate number from image)

10) Apply Tesseract to extract plate number as a text. Tesseract is an optical character recognition (OCR) engine sponsored by Google

APNR: Final Result

Thank you for attention!

Course: Introduction to Intelligent Transportation Systems - GitHub

... Introduction to Intelligent Transportation Systems. University of Tartu, Institute of Computer Science. Project: Automatic Plate Number. Recognition (APNR).

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