本篇論文是藉由RGB-D相機進行在一般環境下的手部範圍截取，並利用此截取的手部影像來進行手勢辨識，但手勢辨識的項目不像其他學者一樣，都是局限於數字(0~9)手語上的辨識，而主要是在於辨識出台灣手語的詞彙，並將這些詞彙中的幾個詞統整在一起，以一句話的方式來進行辨識和呈現。
在應用上，使用者面向相機，在辨識手語過程中，手置於相機距離40cm~70cm的距離之間，先運用深度相機本身的深度大小功能，來截取出此設定範圍間的影像，也就是截取出手部分的影像，再利用Haar分類器技術將所表示出的台灣手語詞彙辨識出來，並標示於影像右上方。在辨識整句台灣手語時，辨識的方式分為前後兩部分，第一部分，僅辨識靜態的台灣手語詞彙所組成的句子，在辨識完成後，會在影像右上方，顯示出整句台灣手語；第二部分，加入動態詞彙台灣手語，來進行辨識整句話的台灣手語，在辨識過程中，因為包含了動態詞彙的台灣手語，所以需要加入光流法，來分辨出手的大致移動方位為何?以辨識出動態詞彙的台灣手語，用以上的方法，就可以進行整句台灣手語的辨識。
本論文最終發展出一套類台灣手語翻譯機的辨識功能，可以進行台灣手語靜態詞彙與動態詞彙的整句台灣手語辨識，而不是單個詞的台灣手語辨識，在論文的發展上，可以說是有所突破。

In this thesis hand images were applied to perform sign language recognition through an RGB-D camera in general environments. Unlike many available methods focusing on number (from zero to nine) recognition in sign language, we proposed a method to perform Taiwanese sign language recognition, both for single vocabulary and sentences.
For practical use, users first put their hands in front of the RGB-D camera with a distance between 40 cm and 70 cm. The depth information extracted from RGB-D camera was then used to construct the hand images and perform Taiwanese sign language vocabulary recognition using Haar feature-based cascade classifiers. The recognition can be classified into two parts. The first part is static Taiwanese sign language recognition for a sentence. The second part is recognizing dynamic Taiwanese sign language vocabularies as a sentence. Because hands are moving during dynamic sign language vocabulary recognition, we applied the optical flow method to recognize the hand orientation. Using the methods above, we have successfully performed Taiwanese sign language recognition. Finally, we also developed a Taiwanese sign language recognition module, which can be treated as a key technology for Taiwanese sign language translation. The recognition includes static and dynamic Taiwanese sign language vocabularies. These results may be useful for future real-time Taiwanese sign language recognition researches.

[1]羅萱蓉, 郭明政, 楊東雲, 高孟育, 柯竹影, 葉梅琳. “手語大師1.” 本公司手語書系研究編輯委員會.紅螞蟻圖書有限公司, 2002.
[2]羅萱蓉, 郭明政, 楊東雲, 高孟育, 柯竹影, 葉梅琳. “我的第一本手語書.” 本公司手語書系研究編輯委員會.紅螞蟻圖書有限公司, 2000.
[3]蔡素娟, 陳怡君. “台灣手語線上辭典-第三版.” 國立中正大學語言學研究所, 2015.
[4]H. Duan, Q. Zhang, and W. Ma. “An Approach to Dynamic Hand Gesture Modeling and Real-time Extraction.” IEEE 3rd International Conference on Communication Software and Networks, pp. 139-142, 2011.
[5]J. Xu, S. Sun, Y. Shi, and Z. Dong. “Implementation of Digital Chime-bell Interaction System Driven by Hand Gesture.” IEEE 3rd International Conference on Pervasive Computing and Applications, pp. 264-268, 2008.
[6]A. Agarwal, and M. K. Thakur. “Sign Language Recognition using Microsoft Kinect.” IEEE 6th International Conference on Contemporary Computing, pp. 181-185, 2013.
[7]Z. Ren, J. Yuan, and Z. Zhang. “Robust Hand Gesture Recognition Based on Finger-Earth Mover’s Distance with a Commodity Depth Camera.” Proceedings of the 19th ACM International Conference on Multimedia, pp. 1093-1096, 2011.
[8]J. Singha, and K. Das. “Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Technique.” International Journal of Advanced Computer Science and Applications, pp. 183-195, 2013.
[9]M. Panwar. “Hand Gesture Recognition based on Shape Parameters.” International Conference on Computing Communication and Applications, pp. 1-6, 2012.
[10]Z. Kalal, K. Mikolajczyk, and J. Matas. “Tracking-learning-detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 7, pp. 1409–1422, 2012.
[11]K. Zhang, L. Zhang, and M.-H. Yang. “Real-time compressive tracking.” Proceedings of the 12th European Conference on Computer Vision, Vol. 3, pp. 864–877, 2012.
[12]G. Allen, R. Y. D. Xu, and J. S. Jin. “Object Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces.” Proceeding of the Pan-Sydney area workshop on Visual Information Processing, pp. 3-7, 2004.
[13]B. D. Lucas, and T. Kanade. “An iterative image registration technique with an application to stereo vision.” Proceeding of the 7th International Joint Conference on Artificial Intelligence, pp. 674–679, 1981.
[14]B.K.P. Horn, and B. Schunk. “Determining Optical Flow.” Artificial Intelligence, Vol. 17, pp. 185-203, 1981.
[15]H. C. Longuet-Higgins. “A Computer Algorithm for Reconstructing a Scene from Two Projections.” Readings in computer vision: Issues, problems, principles, and paradigms, pp. 61–62, 1987.
[16]Z. Zhang. “A flexible new technique for camera calibration.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, pp. 1330-1334, 2000.
[17]D. F. DeMenthon, and L. S. Davis. “Model-based object pose in 25 lines of code.” International Journal of Computer Vision, Vol. 15, pp. 123–141, 1995.
[18]P. Viola, and M. Jones. “Robust real-time face detection.” International Journal of Computer Vision, Vol. 57, pp. 137-154, 2004.
[19]M. Kearns. “The computational complexity of machine learning.” In Cambridge: MIT Press London, England: Massachusetts, 1990.
[20]Y. Freund, and R. E. Schapire. “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting.” Journal of Computer and System Sci-ences, Vol. 55, pp. 119-139, 1997.
[21]N. H. Dardas, and E. M. Petriu. “Hand Gesture Detection and Recognition Using Principal Component Analysis.” IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Proceedings, pp. 1-6, 2011.
[22]W. Yuan, and L. Jing. “Hand-Shape Feature Selection and Recognition Perfermance Analysis.” Proceedings of International Conference on Hand-Based Biometrics, pp. 1-6, 2011.
[23]M. Panwar. “Hand Gesture Recognition based on Shape Parameters.” International Conference on Computing Communication and Applications, pp. 1-6, 2012.
[24]P. Kumar, S. S. Rautaray, and A. Agrawal. “Hand Data Glove: A New Generation Real-Time Mouse for Human-Computer Interaction.” International Conference on Recent Advances in Information Technology, pp. 750-755, 2012.
[25]C. P. Chen, Y. T. Chen, P. H. Lee, Y. P. Tsai, and S. Lei. “Real-time Hand Tracking on Depth Images.” IEEE Visual Communications and Image Processing, pp. 1-4, 2011.
[26]A. Agrawal, R. Raj, and S. Porwal. “Vision-based Multimodal Human-Computer Interaction using Hand and Head Gestures.” IEEE Conference on Information and Communication Technologies, pp. 1288-1292, 2013.