https://github.com/rutujar/vnpr

final year project
https://github.com/rutujar/vnpr

final project year

Last synced: 8 months ago
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final year project

• Host: GitHub
• URL: https://github.com/rutujar/vnpr
• Owner: rutujar
• Created: 2019-05-10T16:33:06.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2020-01-27T08:31:28.000Z (over 6 years ago)
• Last Synced: 2025-10-10T08:33:57.371Z (8 months ago)
• Topics: final, project, year
• Language: MATLAB
• Homepage:
• Size: 33.5 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# VNPR(Vehicle Number Plate Recognition)

# Project Objectives

* Developed a program to recognize automatic license plates based on limited dataset.

* Localized license plate by applying morphological operations and utilizing contour properties.

* Segmented character-like regions of the license plates by applying perspective transforms, performing a connected component analysis, and utilizing contour properties.

* Scissored the true characters from previous step by pruning extraneous license plate character candidates, and extracting each character from binary image to create a training set for building classifiers.

* Extracted BBPS features from the training set and Built two SVM classifiers for recognizing the letters and numbers of the license plate.

# Software/Packages Used

# Algorithms & Methods Used

* License plate localization

  * Apply morphological operations to reveal possible license plate region.

    * Blackhat operation

    * Sobel gradient

    * Otsu automatic thresholding

    * Erosion & dilation

  * Utilize contour properties to prune license plate candidates.

* Characters segmentation

  * Apply perspective transform to extract license plate region from car, obtaining a top-down, bird’s eye view more suitable for character segmentation.

    * 4-point transform

    * Adaptive thresholding

  * Perform a connected component analysis on the license plate region to find character-like sections of the image.

    * 8-connectivity component analysis

    * Convex hull

  * Utilize contour properties to segment the foreground license plate characters from the background of the license plate.

* Character Scissoring

  * Develop and implement a heuristic to prune extraneous license plate character candidates, leaving with only the real characters.

  * Define a method to extract each of the license plate characters from the binary image.

* Character Classification

  * Extract and label license character examples from license plate dataset.

  * Extract block-binary-pixel-sum (BBPS) features from real-world license plate character examples.

    * Block-binary-pixel-sum descriptor

  * Train two classifiers on the BBPS features: one classifier for letter recognition and a second classifier for digit recognition.

    * Support vector machine

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