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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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Impact Factor : 8.14
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| Paper Title: | Sign-To-Speech Technology: Enhancing Accessibility And Inclusion |
| Authors Name: | Vaishali R. Shirkol , Monica K.P , Thajunnisa N M , Sachin K |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_213447
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| Published Paper Id: | IJRTI2606070 |
| Published In: | Volume 11 Issue 6, June-2026 |
| DOI: | |
| Abstract: | Individuals with hearing and speech disabilities often encounter challenges when communicating with people who are unfamiliar with sign language. To address this issue, this project presents a real-time Sign Language to Speech Conversion System that translates hand gestures into spoken words using computer vision and deep learning techniques. The proposed system utilizes a Raspberry Pi connected to a camera for capturing live hand gesture images. These images are analyzed using OpenCV for image processing and MediaPipe for efficient hand landmark detection and tracking. Before recognition, the captured frames undergo several preprocessing operations, including image scaling, normalization, and background refinement, to improve the quality of the input data. The extracted hand features are then provided to a Convolution Neural Network (CNN), which has been trained to identify various sign language gestures. Once a gesture is recognized, the corresponding text representation is generated and converted into speech using a text-to-speech engine. The generated audio is played through an attached speaker, enabling seamless communication between sign language users and non-sign language users. The system is capable of operating in real time and supports the recognition of frequently used gestures with reliable accuracy. By combining embedded computing, computer vision, and machine learning, the proposed solution offers a compact, cost-effective, and user-friendly assistive technology. The developed framework demonstrates the potential of intelligent human-computer interaction systems to improve accessibility, promote social inclusion, and support independent communication for individuals with hearing and speech impairments in everyday environments. |
| Keywords: | Sign Language Recognition, Raspberry Pi, Computer Vision, CNN, OpenCV, MediaPipe, Hand Gesture Detection, Text-to-Speech Conversion, Deep Learning, Assistive Communication System. |
| Cite Article: | "Sign-To-Speech Technology: Enhancing Accessibility And Inclusion", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.a693-a700, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606070.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2606070
Registration ID:213447
Published In: Volume 11 Issue 6, June-2026
DOI (Digital Object Identifier):
Page No: a693-a700 Country: Uttara Kannada, Karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2606070 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2606070 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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