Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices
Author(s) -
Christian Gerber,
Mokdong Chung
Publication year - 2016
Publication title -
journal of information processing systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.288
H-Index - 23
eISSN - 2092-805X
pISSN - 1976-913X
DOI - 10.3745/jips.04.0022
Subject(s) - computer science , convolutional neural network , character (mathematics) , optical character recognition , character recognition , artificial intelligence , pattern recognition (psychology) , geometry , mathematics , image (mathematics)
In this paper, we propose a method to achieve improved number plate detection for mobile devices by applying a multiple convolutional neural network (CNN) approach. First, we processed supervised CNN-verified car detection and then we applied the detected car regions to the next supervised CNN-verifier for number plate detection. In the final step, the detected number plate regions were verified through optical character recognition by another CNN-verifier. Since mobile devices are limited in computation power, we are proposing a fast method to recognize number plates. We expect for it to be used in the field of intelligent transportation systems.
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