| dc.description.abstract | Abstract: Sign language is a visual language
used by people with speech and hearing
disabilities for communication in their daily
conversation activities. It is completely an
optical communication language through its
native grammar. In this paper, hoping to
present an optimal approach, whose major
objective is to accomplish the transliteration
of 24 static sign language alphabet words and
numbers of Srilankan Sign Language into
humanoid or machine decipherable English
manuscript in the real-time environment.
Since Srilanka has a native sign language
deaf/Signers become uncomfortable when
expressing their ideas to a normal person
which is why this system is proposed.
Artificial Neural Networks (ANN) and
Support Vector machines (SVM) have been
used as the technologies of this proposed
system. Pre-processing operations of the
signed input gestures are done in the first
phase. In the next phase, the various region
properties of the pre-processed gesture
images are computed. In the final phase,
based on the properties calculated of the
earlier phase, the transliteration of signed
gesture into text and voice is carried out. The
proposed model is developed using Python
and Python libraries like OpenCV, Keras, and
Pickle. | en_US |
