Abstract:
Sign language is a method of communication primarily used by the hearing impaired and mute
community. In this method, a letter is expressed by hand gestures. Meaningful words can
be constructed by signaling multiple letters in a sequence. This is known as fingerspelling.
For a non-sign-language speaker it is difficult to communicate with someone well-versed in
sign language without assistance from professional interpreters. Therefore, it is worthwhile to
develop a system which allows a non-sign-language speaker to understand the fingerspelling of
a sign language.
In this work a system has been developed to detect fingerspelling in American Sign Language
(ASL) and Bengali Sign Language (BdSL) using data gloves. A data glove is just a glove which
has a number of sensors attached to it. This study identifies a way to construct a suitable
glove for both the languages. The methodologies employed can be used in resource-constrained
environments. The system is capable of detecting both static and dynamic symbols in the
alphabets. Furthermore this work presents a novel approach to perform continuous assessment
of symbols from a stream of run-time data.
