| dc.description.abstract |
Sign language is the primary means of communication for the hard to
hear and speak people around the globe. Sign language emphasizes on visual
possibilities as the participants are unable to hear sound patterns. Sign language
uses different signs, body postures and gestures as opposed to sound patterns
for communication, and evolves like any other spoken language. American
Sign Language (ASL), British sign language (BSL), Arabic sign language
(ArSL), Chinese sign language (CSL) and Indian sign language (ISL) are some
of the widely used sign language systems around the world. There exists
significant variation between sign languages, and due to these inherent
variations, it is not possible to fully adopt a methodology that is found suitable
for all. There are enormous complexities in ISL. Contrary to ASL, ISL
sentences follow Subject-Object-Verb pattern. For example, the relative
positioning of hand on face with respect to nose can convey ‘WOMAN’ or
‘THINK’ in ISL. Such complexities necessitate independent research in ISL.
Sign language recognition involves integration of different categories
of signs. The signs can be mainly categorized into three groups like static hand
gestures, dynamic gestures and facial expression. This research focuses on
these three different channels and work to identify the potential of different
computational methods to address some of the associated complexities with
each channel. These complexities include static gestures with resemblances,
static overlaid gestures, differential movement and directional changes in
dynamic gestures and facial expression changes. |
en_US |