Sign Language Detection Using Deep Learning

Abstract—“Sign Language Detection Using Deep Learning” will determine the next step which is to develop a machine learning model capable of asserting sign, a medium of communication used by the deaf and hard of hearing. The suggested system would be in real time system where live sign gestures would be processed by the use of image processing. There would be the use of the classifiers to distinguish different signs and the output would be showing text. The aim of the system is the enhancement of the current system on this issue in the aspects of reaction speed and accuracy, through efficient algorithms, high-quality data sets and superior sensors. We expect that in our project we evolve a cognitive system that is responsive, robust, to such extent that it be applied in day-to-day tasks and operations by hearing or speech disabled individuals. Keywords—Sign Language Detection, Deep Learning, Image Processing, Real-Time Gesture Recognition, Assistive Technology, Deaf and Hard of Hearing Communication

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-10-12
DOI
https://doi.org/10.55041/ijsrem53008
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Sign Language Detection Using Deep Learning

D Sneha, T S Varsha, Thalari Sumalatha, Ashwini SP et al.
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Hand Gesture Recognition Systems
article

Sign Language Detection Using Deep Learning

D Sneha, T S Varsha, Thalari Sumalatha, Ashwini SP, Tanya D mulani
article en

Abstract

Abstract—“Sign Language Detection Using Deep Learning” will determine the next step which is to develop a machine learning model capable of asserting sign, a medium of communication used by the deaf and hard of hearing. The suggested system would be in real time system where live sign gestures would be processed by the use of image processing. There would be the use of the classifiers to distinguish different signs and the output would be showing text. The aim of the system is the enhancement of the current system on this issue in the aspects of reaction speed and accuracy, through efficient algorithms, high-quality data sets and superior sensors. We expect that in our project we evolve a cognitive system that is responsive, robust, to such extent that it be applied in day-to-day tasks and operations by hearing or speech disabled individuals. Keywords—Sign Language Detection, Deep Learning, Image Processing, Real-Time Gesture Recognition, Assistive Technology, Deaf and Hard of Hearing Communication

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(10)
Openalex Percentile: Top 71%
Hand Gesture Recognition Systems
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