文章基本信息

标题：ENHANCEMENT OF SINGLE-HANDED BENGALI SIGN LANGUAGE RECOGNITION BASED ON HOG FEATURES
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作者：TASNIM TABASSUM ; IQBAL MAHMUD ; PALASH UDDIN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2020
卷号：98
期号：5
页码：743-756
出版社：Journal of Theoretical and Applied
摘要：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.
关键词：Deaf and Dumb;Bengali Sign Language Recognition;Skin Segmentation;HOG Features;Bengali Sign Language Dataset