Coarse-to-fine cascade adaptations for license plate recognition with convolutional neural networks
First Claim
1. A method for license plate, recognition, said method comprising:
- generating a neural network having a plurality of convolutional filters and at least one fully connected layer and wherein said neural network ends in a plurality of independent classifiers, wherein although said classifiers among said plurality of independent classifiers are independent of one another, said classifiers are capable of being trained jointly together with remaining parameters of said neural network;
training said neural network in a coarse-to-fine manner to perform generic text recognition utilizing a plurality of training samples;
iteratively learning and adapting said neural network; and
applying said neural network to a cropped image of a license plate in order to recognize text associated with said license plate and produce a license plate transcription with respect to said license plate.
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Abstract
Methods and systems for license plate recognition utilizing a trained neural network. In an example embodiment, a neural network can be subject to operations involving iteratively training and adapting the neural network for a particular task such as, for example, text recognition in the context of a license plate recognition application. The neural network can be trained to perform generic text recognition utilizing a plurality of training samples. The neural network can be applied to a cropped image of a license plate in order to recognize text and produce a license plate transcription with respect to the license plate. An example of such a neural network is a CNN (Convolutional Neural. Network).
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Citations
20 Claims
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1. A method for license plate, recognition, said method comprising:
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generating a neural network having a plurality of convolutional filters and at least one fully connected layer and wherein said neural network ends in a plurality of independent classifiers, wherein although said classifiers among said plurality of independent classifiers are independent of one another, said classifiers are capable of being trained jointly together with remaining parameters of said neural network; training said neural network in a coarse-to-fine manner to perform generic text recognition utilizing a plurality of training samples; iteratively learning and adapting said neural network; and applying said neural network to a cropped image of a license plate in order to recognize text associated with said license plate and produce a license plate transcription with respect to said license plate. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for license plate recognition, said system comprising:
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at least one processor; and a computer-usable medium embodying computer program code, said computer-usable medium capable of communicating with said at least one processor, said computer program code comprising instructions executable by said at least one processor and configured for; generating a neural network having a plurality of convolutional filters and at least one fully connected layer and wherein said neural network ends in a plurality of independent classifiers, wherein although said classifiers among said plurality of independent classifiers are independent of one another, said classifiers are capable of being trained jointly together with remaining parameters of said neural network; training said neural network in a coarse-to-fine manner to perform generic text recognition utilizing a plurality of training samples; iteratively learning and adapting said neural network; and applying said neural network to a cropped image of a license plate in order to recognize text associated with said license plate and produce a license plate transcription with respect to said license plate. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable medium storing code representing instructions to cause a process for license plate recognition, said code including code to:
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generate a neural network having a plurality of convolutional filters and at least one fully connected layer and wherein said neural network ends in a plurality of independent classifiers, wherein although said classifiers among said plurality of independent classifiers are independent of one another, said classifiers are capable of being trained jointly together with remaining parameters of said neural network; train said neural network in a coarse-to-fine manner to perform generic text recognition utilizing a plurality of training samples; iteratively learn and adapt said neural network; and apply said neural network to a cropped image of a license plate in order to recognize text associated with said license plate and produce a license plate transcription with respect to said license plate. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification