Open AccessLicense Plate Recognition System Using Artificial Neural Networks
Author(s) -
Türkyılmaz İbrahim,
Kaçan Kirami
Publication year - 2017
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.17.0115.0766
Subject(s) - artificial intelligence , artificial neural network , backpropagation , character (mathematics) , segmentation , pattern recognition (psychology) , skew , image segmentation , computer science , license , perceptron , character recognition , feed forward , computer vision , stage (stratigraphy) , image processing , process (computing) , image (mathematics) , engineering , mathematics , geology , telecommunications , operating system , paleontology , geometry , control engineering
A high performance license plate recognition system (LPRS) is proposed in this work. The proposed LPRS is composed of the following three main stages: (i) plate region determination, (ii) character segmentation, and (iii) character recognition. During the plate region determination stage, the image is enhanced by image processing algorithms to increase system performance. The rectangular license plate region is obtained using edge‐based image processing methods on the binarized image. With the help of skew correction, the plate region is prepared for the character segmentation stage. Characters are separated from each other using vertical projections on the plate region. Segmented characters are prepared for the character recognition stage by a thinning process. At the character recognition stage, a three‐layer feedforward artificial neural network using a backpropagation learning algorithm is constructed and the characters are determined.