Hand Gesture Recognition |
Author(s): |
| Aditya Verma , Acropolis Institute of Technology and Research; Aleena Khan, Acropolis Institute of Technology and Research; Aman Dubey, Acropolis Institute of Technology and Research; Kavita Namdev, Acropolis Institute of Technology and Research; Ritesh Khedekar, Acropolis Institute of Technology and Research |
Keywords: |
| OpenCV, HGR (Hand Gesture Recognition), VE (Virtual Environments), DL (Deep Learning), ER (Entity Relationship), DB (Database), ML (Machine Learning), CNN (Convolutional Neural Networks), ASL (American Sign Language), MLP (Multi-layer Perceptron), HOG (Histogram of Gradients) |
Abstract |
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This paper aims to discuss a gesture recognition approach that focuses on hand gestures. We propose a novel deep learning architecture that uses a Convolutional Neural Network (CNN) and OpenCV library. This HGR based project will be useful for mute people in communicating or expressing themselves. By using our project they’ll be able to give voice to their hand gestures so that other people who may or may not have knowledge about hand gesture language can understand them and communication becomes possible. By using OpenCV to capture the hand gestures and CNN the project will be able to read the hand gestures of the person and produce audio messages as per the recognized hand gestures. We evaluate our approach using a dataset of hand gestures involving either one or both hands simultaneously and compare the proposed approach to another that uses hand-crafted features. |
Other Details |
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Paper ID: IJSRDV8I20695 Published in: Volume : 8, Issue : 2 Publication Date: 01/05/2020 Page(s): 902-908 |
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