Reference Hub

This research has been cited in:

Conference
Neural Network-based Real-Time Recognition of American Sign Language Finger-Spelled Gestures: Bridging Communication Gaps2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)10.1109/ICSSAS57918.2023.10331682
Chapter
Automation of Anomaly Detection in Warehouses: A Machine Learning-Based ApproachProceeding of First Doctoral Symposium on Natural Computing Research10.1007/978-981-33-4073-2_16
Article
Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networksApplied Soft Computing10.1016/j.asoc.2020.106775
Conference
Sign Language Gestures Recognition using CNN and Inception v32024 International Conference on Emerging Smart Computing and Informatics (ESCI)10.1109/ESCI59607.2024.10497401
Article
Hand gesture recognition via enhanced densely connected convolutional neural networkExpert Systems with Applications10.1016/j.eswa.2021.114797
Article
Static and dynamic hand gesture recognition system with deep convolutional levy flight whale optimizationMultimedia Tools and Applications10.1007/s11042-023-15397-8
Conference
Gesture Recognition Based on Improved AlexNet2022 2nd International Conference on Electronic Information Engineering and Computer Technology (EIECT)10.1109/EIECT58010.2022.00056
Article
SDViT: Stacking of Distilled Vision Transformers for Hand Gesture RecognitionApplied Sciences10.3390/app132212204
Article
Sign Language Recognition Method Based on Palm Definition Model and Multiple ClassificationSensors10.3390/s22176621
Chapter
Sign Language Identification Using Image Processing TechniquesImage Processing and Capsule Networks10.1007/978-3-030-51859-2_8
Article
LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random ForestPLOS ONE10.1371/journal.pone.0298699
Chapter
A Comprehensive Review of CNN-Based Sign Language Translation SystemProceedings of Data Analytics and Management10.1007/978-981-19-7615-5_31
Conference
Evaluation of Machine Learning Models for Real-Time Sign Recognition2021 IEEE Mysore Sub Section International Conference (MysuruCon)10.1109/MysuruCon52639.2021.9641518
Article
Social IoT Approach to Cyber Defense of a Deep-Learning-Based Recognition System in Front of Media Clones Generated by Model Inversion AttackIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2022.3220080
Conference
Recognition of Indian Sign Language using Machine Learning Algorithms2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)10.1109/SPIN52536.2021.9566141
Article
HGR-ViT: Hand Gesture Recognition with Vision TransformerSensors10.3390/s23125555
Chapter
Acute-Lymphoblastic Leukemia Detection Through Deep Transfer Learning Approach of Neural NetworkProceeding of First Doctoral Symposium on Natural Computing Research10.1007/978-981-33-4073-2_17
Conference
HGR-ResNet: Hand Gesture Recognition with Enhanced Residual Neural Network2023 11th International Conference on Information and Communication Technology (ICoICT)10.1109/ICoICT58202.2023.10262710
Conference
Cost optimization in multi server queueing model with balkingADVANCES IN INTELLIGENT APPLICATIONS AND INNOVATIVE APPROACH10.1063/5.0139432
Article
Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning ApproachesAdvances in Human-Computer Interaction10.1155/2024/1487500
Article
American Sign Language recognition using Support Vector Machine and Convolutional Neural NetworkInternational Journal of Information Technology10.1007/s41870-021-00617-x
Article
Emotion detection from multilingual audio using deep analysisMultimedia Tools and Applications10.1007/s11042-022-12411-3
Article
A simple and effective static gesture recognition method based on attention mechanismJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.103783
Article
Intelligent hybrid hand gesture recognition system using deep recurrent neural network with chaos game optimisationJournal of Experimental & Theoretical Artificial Intelligence10.1080/0952813X.2023.2183269
Conference
American Sign Language Recognition using Video Vision Transformers2023 International Conference on Advanced Computing Technologies and Applications (ICACTA)10.1109/ICACTA58201.2023.10392991
Article
Building of an edge enabled drone network ecosystem for bird species identificationEcological Informatics10.1016/j.ecoinf.2021.101540
Conference
A Smart Sign Language Interpreter for Medical Environments Using Deep Learning: Morocco Case Study2024 2nd International Conference on Mechatronics, Control and Robotics (ICMCR)10.1109/ICMCR60777.2024.10482238
Chapter
Indian Sign Language to Speech Conversion Using Deep LearningInvestigations in Pattern Recognition and Computer Vision for Industry 4.010.4018/978-1-6684-8602-3.ch004

Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

Ravinder Ahuja, Daksh Jain, Deepanshu Sachdeva, Archit Garg, Chirag Rajput

Source Title: International Journal of Ambient Computing and Intelligence (IJACI)10(3)

ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522565086|DOI: 10.4018/IJACI.2019070104

Cite Article Cite Article

MLA

Ahuja, Ravinder, et al. "Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition." IJACI vol.10, no.3 2019: pp.60-73. http://doi.org/10.4018/IJACI.2019070104

APA

Ahuja, R., Jain, D., Sachdeva, D., Garg, A., & Rajput, C. (2019). Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition. International Journal of Ambient Computing and Intelligence (IJACI), 10(3), 60-73. http://doi.org/10.4018/IJACI.2019070104

Chicago

Ahuja, Ravinder, et al. "Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition," International Journal of Ambient Computing and Intelligence (IJACI) 10, no.3: 60-73. http://doi.org/10.4018/IJACI.2019070104

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

MLA

APA

Chicago

Export Reference

Abstract

Request Access