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Enhancement of single-handed Bengali sign language recognition based on HOG features

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Version 2 2024-06-06, 05:17
Version 1 2021-03-25, 10:51
journal contribution
posted on 2024-06-06, 05:17 authored by T Tabassum, I Mahmud, MDP Uddin, ALI Emran, MIBN Afjal, AM Nitu
© 2005 - ongoing JATIT & LLS. Deaf and dumb people usually use sign language as a means of communication. This language is made up of manual and non-manual physical expressions that help the people to communicate within themselves and with the normal people. Sign language recognition deals with recognizing these numerous expressions. In this paper, a model has been proposed that recognizes different characters of Bengali sign language. Since the dataset for this work is not readily available, we have taken the initiative to make the dataset for this purpose. In the dataset, some pre-processing techniques such as Histogram Equalization, Lightness Smoothing etc. have been performed to enhance the signs' image. Then, the skin portion from the image is segmented using YCbCr color space from which the desired hand portion is cut out. After that, converting the image into grayscale the proposed model computes the Histogram of Oriented Gradients (HOG) features for different signs. The extracted features of the signs' are used to train the K-Nearest Neighbors (KNN) classifier model which is used to classify various signs. The experimental result shows that the proposed model produces 91.1% accuracy, which is quite satisfactory for real-life setup, in comparison to other investigated approaches.

History

Journal

Journal of theoretical and applied information technology

Volume

98

Pagination

743-756

Location

Islamabad, Pakistan

Open access

  • Yes

ISSN

1992-8645

eISSN

1817-3195

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

5

Publisher

Little Lion Scientific

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