Comparative Study of Character Recognition on Thai License Plate using DCT and FIR System

被引:0
作者
Sirisantisamrid, Kaset [1 ]
Wongvanich, Napasool [1 ]
Gulpanich, Suphan [1 ]
机构
[1] King Mongkuts Inst Technol Ladkrabang, Fac Engn, Dept Instrumentat & Control Engn, Bangkok 10520, Thailand
来源
2018 18TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS) | 2018年
关键词
separation of DCT coefficients; power spectrum of DCT; FIR system; SEGMENTATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Thai characters have rather similar pes. which they arc not same as English characters that have distinct shapes. Thereby, performing of Thai character recognition is not simple, In this paper, the recognition of characters and numbers on Thai license plate using DCT and FIR system methods is proposed and compared base on recognition rate, There are two ways of studying in DCT method, First, compute the coefficients of 1D-DCT by separation in the horizontal and vertical complements of character and represent them as the features of character. Second, combine the coefficients of 1D-DCT at each component in form of power spectrum of DCT and use it as the character features, For FIR system method, the impulse responses of FIR system are used as the features of character. The 120 car images under variant illumination conditions are used to testing all three methods. Moreover, some images are incomplete conditions such peeling paint, stained with slush and so on. In the results, the success of recognition rate for separation of DCT coefficients, power spectrum of DCT and FIR system methods was 99.15%. 48.30% and 98.30%, respectively.
引用
收藏
页码:650 / 655
页数:6
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[31]   A Study of Artificial Neural Network Classification Using Binary Image Representation for Printed Odia Character Recognition [J].
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[32]   A comparative study on feature selection for retinal vessel segmentation using ant colony system [J].
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Azar, Ahmad Taher ;
Hassaanien, Aboul Ella Otifey .
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[33]   Enhanced brain tumor detection and classification using a deep image recognition generative adversarial network (DIR-GAN): a comparative study on MRI, X-ray, and FigShare datasets [J].
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Kumareshan, N. .
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