Method and system for OCR-free vehicle identification number localization
First Claim
1. A method for localizing numbers and characters in captured images, said method comprising:
- training a machine learning classifier in an offline training phase;
automatically preprocessing a side image of a vehicle digitally captured by at least one camera to determine at least one region of interest in said side image, said at least one region of interest among regions of interest, said preprocessing comprising computer vision including median filtering to remove salt and pepper noise from said side image;
determining by said machine learning classifier a confidence of a series of windows within said regions of interest of said side image, said regions of interest comprising regions of interest of different sizes and aspect ratios, said regions of interest containing a structure of interest in said side image, said confidence comprising a measure of certainty and said structure of interest comprising text including numbers and/or an image of a physical structure of at least one object contained in said side image;
identifying highest confidence candidate regions among said regions of interest that have said structure of interest and identifying at least one region adjacent to said highest confidence candidate regions;
performing an optical character recognition in said at least one adjacent region; and
returning an identifier from said at least one adjacent region in order to localize numbers and characters in said side image of said vehicle.
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Accused Products
Abstract
Methods and systems for localizing numbers and characters in captured images. A side image of a vehicle captured by one or more cameras can be preprocessed to determine a region of interest. A confidence value of series of windows within regions of interest of different sizes and aspect ratios containing a structure of interest can be calculated. Highest confidence candidate regions can then be identified with respect to the regions of interest and at least one region adjacent to the highest confidence candidate regions. An OCR operation can then be performed in the adjacent region. An identifier can then be returned from the adjacent region in order to localize numbers and characters in the side image of the vehicle.
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Citations
20 Claims
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1. A method for localizing numbers and characters in captured images, said method comprising:
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training a machine learning classifier in an offline training phase; automatically preprocessing a side image of a vehicle digitally captured by at least one camera to determine at least one region of interest in said side image, said at least one region of interest among regions of interest, said preprocessing comprising computer vision including median filtering to remove salt and pepper noise from said side image; determining by said machine learning classifier a confidence of a series of windows within said regions of interest of said side image, said regions of interest comprising regions of interest of different sizes and aspect ratios, said regions of interest containing a structure of interest in said side image, said confidence comprising a measure of certainty and said structure of interest comprising text including numbers and/or an image of a physical structure of at least one object contained in said side image; identifying highest confidence candidate regions among said regions of interest that have said structure of interest and identifying at least one region adjacent to said highest confidence candidate regions; performing an optical character recognition in said at least one adjacent region; and returning an identifier from said at least one adjacent region in order to localize numbers and characters in said side image of said vehicle. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system for localizing numbers and characters in captured images, said system comprising:
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at least one camera; a processor that communicates with said at least one camera; and a non-transitory computer-usable medium embodying computer program code, wherein said computer-usable medium communicates with the processor, said computer program code comprising instructions executable by said processor and configured for; training a machine learning classifier in an offline training phase; automatically preprocessing a side image of a vehicle digitally captured by said at least one camera to determine at least one region of interest in said side image, said at least one region of interest among regions of interest, said preprocessing comprising computer vision including median filtering to remove salt and pepper noise from said side image; determining by said machine learning classifier a confidence of a series of windows within said regions of interest of said side image, said regions of interest comprising regions of interest of different sizes and aspect ratios, said regions of interest containing a structure of interest in said side image, said confidence comprising a measure of certainty and said structure of interest comprising text including numbers and/or an image of a physical structure of at least one object contained in said side image; identifying highest confidence candidate regions among said regions of interest that have said structure of interest and identifying at least one region adjacent to said highest confidence candidate regions; performing an optical character recognition in said at least one adjacent region; and returning an identifier from said at least one adjacent region so as to localize numbers and characters in said side image of said vehicle. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory processor-readable medium storing code representing instructions to cause a computer executable process for localizing numbers and characters in captured images to:
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train a machine learning classifier in an offline training phase; automatically preprocess a side image of a vehicle digitally captured by at least one camera to determine at least one region of interest in said side image, said at least one region of interest among regions of interest, said preprocessing comprising computer vision including median filtering to remove salt and pepper noise from said side image; determine by said machine learning classifier a confidence of a series of windows within said regions of interest of said side image, said regions of interest comprising regions of interest of different sizes and aspect ratios, said regions of interest containing a structure of interest in said side image, said confidence comprising a measure of certainty and said structure of interest comprising text including numbers and/or an image of a physical structure of at least one object contained in said side image; identify highest confidence candidate regions among said regions of interest that have said structure of interest and identifying at least one region adjacent to said highest confidence candidate regions; perform an optical character recognition in said at least one adjacent region; and return an identifier from said at least one adjacent region in order to localize numbers and characters in said side image of said vehicle. - View Dependent Claims (18, 19, 20)
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Specification