Sign Language Recognition System For Deaf And Dumb People

Mohd Hasni, Mohd Fareed Asyraf (2015) Sign Language Recognition System For Deaf And Dumb People. Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text Sign Language Recognition System For Deaf And Dumb People 24 Pages.pdf - Submitted Version Download (702kB)

Abstract

Every normal human being sees, listens, and reacts to surrounding. There are some unlucky individuals who does not have this important blessing. Such individuals, mainly deaf and dumb, they depend on communication via sign language to interact with others. However, communication with ordinary individuals is a major impairment for them since not every typical people comprehend their sign language. Furthermore, this will cause a problem for the deaf and dumb communities to interact with others, particularly when they attempting to involve into educational, social and work environments. In this project, the objectives are to develop a sign language translation system in order to assist the hearing or speech impaired people to communicate with normal people, and also to test the accuracy of the system in interpreting the sign language. For the methodology, several researches have been done with a specific end goal to choose the best method in gesture recognition, and the data glove approach may be the champion. The configuration of the data glove includes 10 tilt sensor to capture the finger flexion, an accelerometer for recognizing the orientation of the hand, Arduino Leonardo function as microcontroller and Bluetooth module is use to establish connection between Arduino and mobile phone. The first experiment is to test the performance of the tilt sensor. While second experiment is to test the accuracy of the data glove in translating several alphabets, numbers and words from Malaysian Sign Language. The result for the first experiment shows that tilt sensor need to be tilted more than 85 degree to detect the tilt. The result for the second experiment shows that total average accuracy for translating alphabets is 95%, numbers is 93.33% and gestures is 78.33%. For the average accuracy of data glove for translating all type of gestures is 89%.

Actions (login required)

View Item