在電腦視覺領域中的人機介面系統，手勢與手語辨識一直是非常熱門的研究主題之一，眾多研究的成果可以被廣泛地應用在某些特定領域上，然而大多數研究還是以個人電腦為基礎架構，在經過影像的輸入和輸出以及作業系統的運算，都會造成電腦內的中央處理器龐大負擔，導致整體系統效率降低，不如我們預期想要達到的效能。因此近年來，由於數位訊號處理器擁有體積小且對大量且複雜的運算處理有著高效能的表現，所以本論文結合德州儀器生產的數位訊號處理器TMS320DM642 EVM與CCS(Code Composer Studio)軟體系統當作是我們的開發平台。首先使用移動物體偵測判斷手勢目前的狀態，判斷手勢靜止後才抓取目前影像，接著利用手腕切割(Wrist Cropped)去除手腕以下的部分，以得到手勢，手勢得到後，先使用角度分類將手勢分為兩大群組，第二群組使用特徵辨識，第一群組經由細線化(Thinning)簡化手勢，再來使用細線化後的手勢影像以及前處理過後的手勢特徵影像將手勢分類與辨識，最後辨識結果經由OSD(On Screen Display)顯示在螢幕上，如此一來就能達到即時美國文字手語辨識的任務。

Both Hand gesture and sign language are very popular research in the filed of computer vision for human interface system. The accomplishments of the research can be widely applied to some specific fields. However, most of the digital image process systems are based on personal computer. After input and output of images and the operations of Operating System, these procedures will cause heavy loading of the CPU and lower efficiency of the system. Hence, the result doesn’t meet what we expected. In recent years, due to the development of DSP bring many benefits such as small size and high performance of the complicated operation algorithms, we integrate the TI TMS320DM642 EVM with CCS software system as our research developing platform. First of all, we determine that whether the gesture is moving or not by utilizing moving edge detection until the gesture is motionless. Secondly, we capture the image and cut the part below the wrist by utilizing wrist cropped algorithm. Thirdly,we utilize orientation to classify gestures into two classifications, one is recognized by characteristics ,another is recognize by utilizing the thinning algorithm to simplify the gestures, and then classify and recognize based on gestures image alter thinning and gesture characteristic image after preprocess. Finally, the results of recognition are displayed on the monitor by OSD. We complete the real time American Sign Language recognition system at the end.

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