Sign language is a communication tool for people with deaf and mute conditions. It is a set of hand gestures used by deaf and mute people to communicate. Deaf and mute people face a lot of difficulties when communicating with ordinary people and struggle to learn new skills from others. Many researchers have carried out various approaches to solve the problems faced by deaf and mute people. Researchers have also focused on problems faced by deaf and mute children while learning. Most of the researchers have focused on teaching sign language. Providing a feedback mechanism is not well explored. Recognizing static and dynamic sign languages and providing feedback is a challenging problem. Sign language is not the same in every country. Therefore, a solution designed for one sign language can’t be used to solve problems faced by another sign language. In this research, a web application called Esign Guru is developed to teach Sri Lankan static and dynamic sign language while also proving practice and feedback mechanisms for each sign language. The proposed system uses machine learning techniques to recognize sign language performed by the user. The system has a text-to-sign language summary module, which helps the students to learn the sign language summary for the given text. Esign Guru is a web application, which can be accessed via any browser without any special devices which makes it a cost-effective solution.

Static sign, dynamic sign, sign recognition, sign language practice, text-to-sign

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