Sign language are used worldwide by many of individuals. They are mostly used by the deaf communities and their teachers, or people associated with them by ties of friendship or family. Speakers are a minority of citizens, often isolated, technically they are lack of attention in such forms of communication. Actually, there is some, but limited research and development in computer vision. In particular case of Ethiopian Sign language there is no an efficient system to perform the real time recognition of Ethiopian sign language. With advance of technology, there are new possibilities to find or solve this problem. In this thesis, we develop a prototype and contribute to the problem of gesture recognition apply to Ethiopian sign language. In this work we convert the hand gesture into text and voice that is Ethiopian sign language to Amharic text and voice. In this work computer vision technique is adapted, and capturing, pre-processing, feature extraction, classification/identification also present. Open-source computer vision supports us to implement our protype. Typically, Open-Source computer vision (OpenCV) provides with better image or video processing function that helps the hand to process images using different approaches including recognition, detection and reconstruction. These approach helps in identification and detection of hand signs and obtain the related text that people (lack of sign language) can be understand. In this study we have done a real time prototype that convert Ethiopian Sign Language (ETHSL) to text for six Amharic letters and for six Numbers (0-5) using web camera and computer vision techniques. When we evaluate the efficiency sign for number zero and sign for number 1 has highest efficiency (higher value of efficiency is better) followed by sign 2 and the sign 4 while sign for number 5 has the least efficiency. On Amharic letter side, sign for ምልክት አ has highest efficiency (higher value of efficiency is better) followed by ምልክት ሀ while sign for ምልክት ሐ has the least efficiency. Accuracy of the prototype for Realtime conversion of sign language for Amharic letter into text is 83.3% and sign language for Number into text is 76% for recognition. Limitations are due to lower web camera resolution or physical strain of training dataset. Overall, the work Realtime conversion system provides an easy to use and accurate Sign language input and modality without placing restriction on users.