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Identifying License Plate Character Segmentation Boundaries Using Convolutional Neural Networks

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dc.contributor.advisor Dailey, Matthew
dc.contributor.author Bazard, Benoit
dc.contributor.other Ekpanyapong, Mongkol
dc.contributor.other Esichaikul, Vatcharaporn
dc.date.accessioned 2017-05-15T03:14:54Z
dc.date.available 2017-05-15T03:14:54Z
dc.date.issued 2017-05
dc.identifier.other AIT
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/856
dc.description 34 p. en_US
dc.description.abstract The aim of the present paper is to design a way to perform character segmentation on license plates using convolutional neural networks without explicitely performing character recognition so as to keep the segmentation and the recognition distincts. This paper details the implementation of the algorithm and an evaluation of its global performance on a dataset of 545 license plates provided by the Vision and Graphics Lab at AIT. The system successively applies two convolutional neural networks to perform vertical segmentation and then horizontal segmentation. The results suggest that convolutional neural networks on their own are not enough to achieve acceptable performance on a dataset of this size. A more sophisticated classifier is necessary to convert the features given by the neural networks into robust character boundaries. The main improvement needed is to eliminate non-character regions identified by the segmenter. en_US
dc.description.sponsorship TELECOM SudParis en_US
dc.language.iso en en_US
dc.publisher AIT en_US
dc.subject neural network en_US
dc.subject convolutional neural network en_US
dc.subject license plate en_US
dc.subject segmentation en_US
dc.title Identifying License Plate Character Segmentation Boundaries Using Convolutional Neural Networks en_US
dc.type Thesis en_US

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