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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | An Integrated CNN-Hand Landmark Feature Framework for Multi-Language Sign Language Recognition |
| Authors Name: | Yangala Shashidhar Reddy , Bellamkonda Masthan Basha , Vadla Meghana , Dr. S. Karimulla Basha |
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IJRTI_211116
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| Published Paper Id: | IJRTI2604064 |
| Published In: | Volume 11 Issue 4, April-2026 |
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| Abstract: | The use of sign language recognition systems is critical in enhancing communication among the deaf and hard-of- hearing. This project expands an existing multi-modal American Sign Language (ASL) recognition framework into a multi- language scalable sign language recognizer. The proposed method integrates Convolutional Neural Network (CNN)-based visual feature extraction with hand landmark-based structural features to improve the accuracy and robustness of gesture classification. By combining image representations and skeletal features, the system effectively handles challenges such as variations in light- ing, background noise, and hand orientation. Unlike traditional systems that are limited to a single language, the proposed framework supports multiple sign languages, in- cluding American Sign Language (ASL), Indian Sign Language (ISL), and Arabic Sign Language (ArSL). A modular architecture is designed to ensure flexibility and scalability, where separate trained models are maintained for each language. A user-friendly interface enables dynamic language selection, allowing the system to load and execute the appropriate model in real time. The system processes live webcam input and translates recog- nized gestures into text output, enabling seamless communication. This design not only enhances accuracy but also improves usability and adaptability in real-world scenarios. Overall, the proposed system presents an efficient and practical solution for multilingual sign language recognition, making it suitable for diverse cultural and communication environments. |
| Keywords: | Sign Language Recognition, Convolutional Neural Networks (CNN), Hand Landmark Detection, Multi-Modal Learning, Real-Time Gesture Recognition, American Sign Language (ASL), Indian Sign Language (ISL), Arabic Sign Language (ArSL). |
| Cite Article: | "An Integrated CNN-Hand Landmark Feature Framework for Multi-Language Sign Language Recognition", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a472-a479, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604064.pdf |
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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: IJRTI2604064
Registration ID:211116
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a472-a479 Country: Kadapa, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604064 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604064 |
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ISSN: 2456-3315
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