International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1907 Sign Language Interpreter using Image Processing and Machine Learning Omkar Vedak 1 , Prasad Zavre 2 , Abhijeet Todkar 3 , Manoj Patil 4 1,2,3 Student, Department of Computer Engineering, Datta Meghe College of Engineering, Mumbai University, Airoli, India 4 Assistant Professor, Department of Computer Engineering, Datta Meghe College of Engineering, Mumbai University, Airoli, India ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Speech impairment is a disability which affects one’s ability to speak and hear. Such individuals use sign language to communicate with other people. Although it is an effective form of communication, there remains a challenge for people who do not understand sign language to communicate with speech impaired people. The aim of this paper is to develop an application which will translate sign language to English in the form of text and audio, thus aiding communication with sign language. The application acquires image data using the webcam of the computer, then it is pre- processed using a combinational algorithm and recognition is done using template matching. The translation in the form of text is then converted to audio. The database used for this system includes 6000 images of English alphabets. We used 4800 images for training and 1200 images for testing. The system produces 88% accuracy. Key Words: Pre-processing, Feature Extraction, Edge Detection, Classification. 1. INTRODUCTION Sign language is an important part of life for deaf and mute people. They rely on it for everyday communication with their peers. A sign language consists of a well-structured code of signs, and gestures, each of which has a particular meaning assigned to it. They have their own grammar and lexicon. It includes a mixture of hand positioning, shapes and movements of the hand. The people who know sign language can communicate with each other efficiently. However, when it comes to communicating with people who don’t understand sign language it causes a lot of problems. Communication is a very important part of our lives. We interact with our mates at offices, schools, hospitals and other public places. Deaf and mute people may find it difficult to express themselves in such situations because not everyone understands sign language. There are many highly talented people suffering from speech impediment. We feel that their disability should become a hindrance to achieve their goals. Adding them into the workforce will only improve the socio-economic development of the country. Deaf and mute people usually depend on sign language interpreters for communication. However, finding a good interpreter is difficult and often expensive. Thus, a computerized interpreter could be a reliable and cheaper alternative. A system that can translate sign language into plain text or audio can help in real-time communication. It can also be used to provide interactive learning of sign language. There is no universal sign language for deaf people. Different countries use their own sign language, although there some striking similarities among them. It is yet unclear how many sign languages exist in the world. Some languages have got legal recognition and some have not. India’s National Association of Deaf estimates that there are 18 million people in India with hearing impairment. This paper discusses the implementation of a system which translates Indian Sign Language gestures to its English language interpretation. 2. LITERATURE SURVEY Several types of researches have been done in translating Indian Sign language using deep learning. Some of them used instrument-based approach and some have used a video- based approach. In Ref. [1] Pham The Hai uses Microsoft Kinect to translate Vietnamese Sign Language. In the proposed system, the person has to place himself with Kinect’s field of view and then perform sign language gestures. It can recognize both static and dynamic gestures using multiclass Support Vector Machine. During recognition, the gesture features are extracted, normalized and filtered out based on Euclidean distance. Purva Badhe [2] uses Fourier Descriptor for feature extraction. The system translates Indian Sign language gestures to English language. To represent the boundary points, the Fourier Series were calculated using Fast Fourier Transform (FFT) algorithm. The extracted data being too large is compressed using vector quantization. This data is then stored into a codebook. For testing purpose, the code vector generated from gestures is compared with existing codebook and gesture is recognized.