Hand Gesture Identification for Improving Accuracy Using Convolutional Neural Network(CNN)
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2022.13.247Keywords:
Hand Gesture, Machine Learning, ASL Data Set, Convolutional Neural Network;Dimensions Badge
Issue
Section
License
Copyright (c) 2022 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Hand gestures are a type of non-verbal communication that uses visible body movements to convey important messages. This paper presents a much better approach of hand gesture prediction. Image Identification is an important step in most of the modern hand gesture prediction system. A convolutional neural network are used for improving the accuracy of the system. Proposed system tested for large number of hand gesture images using Tensor flow tool . The convolutional neural network (ConvNet) is a deep learning algorithm for learning and classifying hand gestures and achieved accuracy 93.61%.Abstract
How to Cite
Downloads
Similar Articles
- Seema Bhakuni, Application of artificial intelligence on human resource management in information technolgy industry in India , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Sawitri Devi, Raj Kumar, Unveiling scholarly insights: A bibliometric analysis of literature on gender bias at the workplace , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Brigith Gladys L, J. Merline Vinotha, Sustainable rough multi-objective two-stage solid transportation problem of third-party e-commerce logistic providers with conditional fixed parameter on safety , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper
- Bayelign Abebe Zelalem, Ayalew Ali Abebe, Financial strategy and private commercial banks’ profitability in the emerging market: Evidence from Ethiopia , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- Isreal Zewide, Wondwosen Wondimu, Melash Woldu, Kibnesh Admasu, Maize (Zea mays L.) Productivity as affected by different ratios of fertilizer (blended NPS) and inter row spacing at West Omo, South-West Ethiopia , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Sheena Edavalath, Manikandasaran S. Sundaram, MARCR: Method of allocating resources based on cost of the resources in a heterogeneous cloud environment , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Kunal Lanjekar, Prashant Kalshetti, Joe C. Lopez, Role of social media in lead generation , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Sanskriti Gandhi, Usha Asnani, Srivalli Natarajan, Chinmay Rao, Richa Agrawal, Evaluation of stability of fixation using conventional miniplate osteosynthesis in comminuted and non-comminuted Le Fort I, II, III fractures – A dynamic finite element analysis , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Priya Tiwari, Bharat Kasar, Vibhu Tripathi, Decoding Investor’s behavior in tax saving mutual fund: A multi-item scale for evaluating investors’ category , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Venkatesh R, A study on women empowerment by enhancing saving capabilities – through self-help groups , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
<< < 34 35 36 37 38 39 40 41 42 43 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Rajesh Kumar Singh, Genetic Variability in Aromatic Rice , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper

