號碼布之影像辨識研究

A Study of Image Recognition for Number Tags

林妍均(Yen-Chun Lin)

指導教授 ： 吳毅成

國立交通大學/資訊學院/資訊學院資訊學程/碩士(2016年)

摘要

本論文研究影像中號碼布的辨識方法，主要分為號碼布定位及字元辨識兩個部分。
號碼布定位利用了邊緣偵測(Edge Detection)、形態學(Morphology)中的侵蝕(Erosion)與膨脹(Dilation)再搭配機器學習(Machine learning)中的支持向量機(Support Vector Machine)來完成，使用了4500張影像來做實驗，其查準率(Precision)為98.34%，查全率(Recall)則是98.16%。
字元辨識的部分是使用類神經網路(Artificial Neural Network)中的反向傳播(Back Propagation)來辨識，讓內部自動反饋更新權重，使實際輸出值越來越接近目標輸出值，這個部分使用了5000張影像實驗，其準確率達到了99.07%，而在使用機器學習時，皆利用了逐次半自動遞迴標籤法，大量減少手動標籤的次數。

關鍵字

號碼布定位 ； 字元辨識 ； 機器學習 ； 支持向量機 ； 類神經網路

並列摘要

The purpose of this thesis was to study the method of image recognition for number tags. The major research includes plate localization and character recognition two portions.
Plate localization was applied by lots of methodology, e.g. edge detection, erosion and dilation of morphology, support vector machine of machine learning. The localization experiment was executed by using 4,500 images and it was found that the precision is 98.34%, the recall is 98.16%.
Character recognition was applied by back propagation of artificial neural network. It leveraged internal feedback to automatically update priority weights, therefore the mechanism could reach the target by operating more or more trials. The recognition experiment was executed by using 5,000 images and it was found that the accuracy is 99.07%. The method of applying machine learning was conducted on semi-recursive labeling to significantly reduce the cost of manual labeling.

並列關鍵字

plate localization ； character recognition ； machine learning ； support vector machine ； artificial neural network

參考文獻

[1] Shutao Li and J. T. Kwok, "Text extraction using edge detection and morphological dilation", Intelligent Multimedia, Video and Speech Processing, pp. 330-333, October 2004.

[4] Datong Chena, Jean-Marc Odobeza and Jean-Philippe Thiran, "A localization/verification scheme for finding text in images and video frames based on contrast independent features and machine learning methods", Image Communication Vol. 19, Issue 3, pp. 205-217, March 2004.

[5] Kwang In Kim, Keechul Jung and Jin Hyung Kim, "Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm", IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 25, Issue 12, December 2003.

[6] G. R. Bradski, "Real time face and object tracking as a component of a perceptual user interface", Proc. IEEE Applications of Computer Vision, pp. 214-219, October 1998.

[8] Hongxing Sun, Nannan Zhao and Xinhe Xu, "Extraction of Text under Complex Background Using Wavelet Transform and Support Vector Machine", IEEE International Conference on Mechatronics and Automation, June 2006.