Article Fingerprint
ReserarchID
HU7D1
Crossref Journal DOI 10.17406/gjmra
Print ISSN 0975-5888
e-ISSN 2249-4618
Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.
Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.
This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
Inclusion has become a priority on the global educational agenda,
A deaf-dumb individual always uses gestures to convey his/her ideas to others. However, it is hard for people to understand this gesture language. The purpose of the project is to develop a computer-based system to recognize 26 gestures from American Sign Language (ASL) using MATLAB, which will enable deaf-dumb individuals significantly to communicate with all other people using their natural hand gestures. The proposed system in this project is composed of five modules, which are prepared datasets for ASL which was self-collected using hand gestures from both male and female volunteers, who have alternative ages and skin color in different backgrounds and postures by an ordinary phone camera in total the dataset was 260 images preprocessing, hand segmentation, feature extraction, sign recognition, and text of sign voice conversion. Segmentation is done by converting the image to Hue-Saturation-Value (HSV) format and using color threshold APP. Blob features are extracted by using (BOF) which used the Speed up Robust Features (SURF) algorithm. Furthermore… the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for gesture recognition. The Recognized gesture is converted into voice format.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
