發展自動車牌辨識之機器學習系統

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發展自動車牌辨識之機器學習系統

Development of a Machine Learning System for Automatic License Plate Recognition

范哲綸 , Masters　　Advisor：陳冠宇　　

繁體中文

機器學習；車牌辨識；影像處理； machine learning ； license plate recognition ； image processing

Abstract │ Reference (20) │ Altmetrics

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With the progress of science and technology, the automatic license plate recognition (ALPR) system and its applications have increased noticeably. In general, the ALPR system can be divided into four major stages. These stages are image pre-processing, image calibration, image segmentation and character recognition. The purpose of this thesis is to develop an ALPR system based on neural networks, especially used for enhancing performance of character recognition, by integration of machine vision and machine learning. First, this thesis develops a graphical user interface to facilitate the operation of the ALPR system. Next, car front images can be grabbed by a camera and need to be performed image pre-processing, such as color space transformations, gray scale thresholding, image binarization, edge detection, morphological image processing. Accordingly, we can obtain training samples for used in the learning phase of the ALPR system. Finally, physical testing of the ALPR system in real time is performed to evaluate the performance after the training of the neural network is completed. In this thesis, we tested 96 car front images and the ALPR system achieved a 92% success rate. Some common causes of failure in recognition include characters corrupted by the rust from license plate fixed screws, blurred or unfocused images, and the shooting angle was too skewed. As a whole, if the original quality of grabbed images are good enough, the success rate of the proposed ALPR system can be improved.

Reference ( 20 ) 〈TOP〉

[1] W. McCulloch and W. Pitts, “A logical calculus of the ideal immanent in neurons activity,” Bulletin of Mathematical Biophysics, Vol. 5, pp. 115-133, 1943.
連結：
[2] F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain,” Psychological Review, Vol. 65, No. 6, pp. 386, 1958.
連結：
[3] J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proceedings of the National Academy of Sciences of the USA, Vol. 79, No. 8, pp. 2554-2558, 1982.
連結：
[5] 張簡子，以小腦模型在 FPGA 上作車牌辨識，國立台灣師範大學工業教育所碩士論文，1990。
連結：
[6] 何宇翔，利用階層式類神經網路實現車牌辨識系統，嶺東科技大學資訊科技應用研究所碩士論文，2000。
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