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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