Hand Sign Language Recognition for Bangla Alphabet using Support Vector Machine
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Date
2016-10-28
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
The sign language considered as the main language for
deaf and dumb people. So, a translator is needed when a normal
person wants to talk with a deaf or dumb person. In this paper, we
present a framework for recognizing Bangla Sign Language (BSL)
using Support Vector Machine. The Bangla hand sign alphabets
for both vowels and consonants have been used to train and test
the recognition system. Bangla sign alphabets are recognized by
analyzing its shape and comparing its features that differentiates
each sign. In proposed system, hand signs are first converted to
HSV color space from RGB image. Then Gabor filters are used to
acquire desired hand sign features. Since feature vector obtained
using Gabor filter is in a high dimension, to reduce the
dimensionality a nonlinear dimensionality reduction technique
that is Kernel PCA has been used. Lastly, Support Vector Machine
(SVM) is employed for classification of candidate features. The
experimental results show that our proposed method outperforms
the existing work on Bengali hand sign recognition.
Description
Keywords
Bangla sign language, HSV, Gabor filter, Kernel PCA, Support Vector Machine
Citation
ICISET2016-ID-20