|Multi-Task Convolutional Neural Network System for License Plate Recognition
Hong-Hyun Kim, Je-Kang Park, Joo-Hee Oh, and Dong-Joong Kang*
International Journal of Control, Automation, and Systems, vol. 15, no. 6, pp.2942-2949, 2017
Abstract : "License plate recognition is an active research field as demands sharply increase with the development
of Intelligent Transportation System (ITS). However, since the license plate recognition(LPR) is sensitive to the
conditions of the surrounding environment such as a complicated background in the image, viewing angle and
illumination change, it is still difficult to correctly recognize letters and digits on LPR. This study applies Deep
Convolutional Neural Network (DCNN) to the license plate recognition. The DCNN is a method of which the
performance has recently been proven to have an excellent generalization error rate in the field of image recognition.
The proposed layer structure of the DCNN used in this study consists of a combination of a layer for judging the
existence of a license plate and a layer for recognizing digits and characters. This learning method is based on Multi-
Task Learning (MTL). Through experiments using real images, this study shows that this layer structure classifies
digits and characters more accurately than the DCNN using a conventional layer does. We also use artificial images
generated directly for training model."
Deep convolutional neural network, license plate recognition, machine learning, multi task learning.
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