Robust cropping of license plate images
First Claim
1. A method for cropping of a license plate image, said method comprising:
- identifying a first plurality of details of said license plate image utilizing an image-based classifier that serves as a valid character detector to indicate a subset of said license plate image where character images likely reside wherein said first plurality of details comprise at least one of;
a character, a valid license plate character, a symbol, a logo, a license plate border, a license plate border with poor contrast, and extraneous edge information outside of said license plate;
resizing said subset of said license plate image where character images likely reside to a standard vertical dimension associated with a classifier template;
identifying candidate character locations of said license plate image and determining if said candidate character locations exceed a minimum number of characters;
performing a first cropping of said license plate image including removing tilt from and vertically cropping said license plate image;
identifying a second detail of said license plate image utilizing a gradient-based classifier on said candidate character locations and performing a second cropping of said first cropping of said license plate image based on detected character locations, further comprising a horizontal cropping via projective segmentation of said license plate image based on said detected character locations, if said candidate character locations exceed said minimum number of characters; and
utilizing said identified first plurality of details and said identified second detail to recognize and identify a license plate in said license plate image;
further comprising identifying said first plurality of details and said second detail utilizing a Sparse Network of Winnows classifier, wherein said Sparse Network of Winnows classifier accounts for the following;
varying distances between characters on said license plate, a license plate border, a symbol, and logos on said license plate.
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Accused Products
Abstract
A method, system, and computer-usable tangible storage device for robustly cropping and accurately recognizing license plates to account for noise sources and interfering artifacts are disclosed. License plate images and sub-images can be tightly cropped utilizing an image-based classifier and gradient-based cropping. An image-based classifier can identify the location of valid characters within the image. Because of a number of noise sources, such as, for example, residual plate rotation and shear in the characters within the image, the image-based classifier performs a “rough” identification of the image boundaries. An additional processing step utilizing gradient-based cropping is performed to fine-tune the license plate image boundaries. Gradient-based cropping eliminates unwanted border artifacts that could substantially impact the segmentation and license plate character recognition results.
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Citations
10 Claims
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1. A method for cropping of a license plate image, said method comprising:
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identifying a first plurality of details of said license plate image utilizing an image-based classifier that serves as a valid character detector to indicate a subset of said license plate image where character images likely reside wherein said first plurality of details comprise at least one of;
a character, a valid license plate character, a symbol, a logo, a license plate border, a license plate border with poor contrast, and extraneous edge information outside of said license plate;resizing said subset of said license plate image where character images likely reside to a standard vertical dimension associated with a classifier template; identifying candidate character locations of said license plate image and determining if said candidate character locations exceed a minimum number of characters; performing a first cropping of said license plate image including removing tilt from and vertically cropping said license plate image; identifying a second detail of said license plate image utilizing a gradient-based classifier on said candidate character locations and performing a second cropping of said first cropping of said license plate image based on detected character locations, further comprising a horizontal cropping via projective segmentation of said license plate image based on said detected character locations, if said candidate character locations exceed said minimum number of characters; and utilizing said identified first plurality of details and said identified second detail to recognize and identify a license plate in said license plate image; further comprising identifying said first plurality of details and said second detail utilizing a Sparse Network of Winnows classifier, wherein said Sparse Network of Winnows classifier accounts for the following;
varying distances between characters on said license plate, a license plate border, a symbol, and logos on said license plate. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system for cropping of a license plate image, comprising:
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a processor that communicates electronically with a memory; and a non-transitory computer-usable medium embodying computer program code stored in said memory, said non-transitory computer-usable medium capable of communicating with said processor, said computer program code comprising instructions executable by said processor and configured for; identifying a first plurality of details of said license plate image utilizing an image-based classifier that serves as a valid character detector to indicate a subset of said license plate image where character images likely reside wherein said first plurality of details comprise at least one of; a character, a valid license plate character, a symbol, a special license plate symbol, a logo, a license plate border, a license plate border with poor contrast, and extraneous edge information outside of said license plate; resizing said subset of said license plate image where character images likely reside to a standard vertical dimension associated with a classifier template; identifying candidate character locations of said license plate image and determining if said candidate character locations exceed a minimum number of characters; performing a first cropping of said license plate image including removing tilt from and vertically cropping said license plate image; identifying a second detail of said license plate image utilizing a gradient-based classifier on said candidate character locations and performing a second cropping of said first cropping of said license plate image based on detected character locations, further comprising a horizontal cropping via projective segmentation of said license plate image based on said detected character locations, if said candidate character locations exceed said minimum number of characters; and utilizing said identified first plurality of details and said identified second detail to recognize and identify a license plate in said license plate image; further comprising identifying said first plurality of details and said second detail utilizing a Sparse Network of Winnows classifier, wherein said Sparse Network of Winnows classifier accounts for the following;
varying distances between characters on said license plate, a license plate border, a symbol, and logos on said license plate. - View Dependent Claims (8, 9, 10)
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Specification