Open AccessBest Combination of Binarization Methods for License Plate Character Segmentation
Author(s) -
Yoon Youngwoo,
Ban KyuDae,
Yoon Hosub,
Lee Jaeyeon,
Kim Jaehong
Publication year - 2013
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.13.0112.0545
Subject(s) - artificial intelligence , segmentation , character (mathematics) , computer science , pattern recognition (psychology) , binary number , computer vision , image segmentation , image (mathematics) , binary image , connected component , noise (video) , image processing , mathematics , arithmetic , geometry
A connected component analysis from a binary image is a popular character segmentation method but occasionally fails to segment the characters owing to image noise and uneven illumination. A multimethod binarization scheme that incorporates two or more binary images is a novel solution, but selection of binarization methods has never been analyzed before. This paper reveals the best combination of binarization methods and parameters and presents an in‐depth analysis of the multimethod binarization scheme for better character segmentation. We carry out an extensive quantitative evaluation, which shows a significant improvement over conventional single‐method binarization methods. Experiment results of six binarization methods and their combinations with different test images are presented.