Real-Time Sign Language Interpretation Using Deep Learning Models for Accessible Communication

Abstract

This project presents a real-time American Sign Language (ASL) recognition system using a webcam-based interface and a lightweight Convolutional Neural Network (CNN) model, specifically designed for alphabet gesture classification. The system addresses communication challenges faced by hearing and speech-impaired individuals by enabling seamless gesture-to-text conversion using deep learning. A custom ASL dataset was created using webcam input in a controlled environment to ensure diversity in hand shapes, positions, and backgrounds. To improve gesture segmentation accuracy, YCrCb color space was utilized for effective skin detection. The CNN model was trained to classify 26 ASL alphabet gestures with a remarkable accuracy of 96.3%. Real-time implementation was achieved using OpenCV and TensorFlow on low-cost computing hardware, ensuring accessibility and performance. The system demonstrates stability across varying lighting conditions and hand orientations. It offers potential integration with assistive technologies such as voice converters or mobile applications, thus promoting inclusivity and accessibility in daily communication. This work contributes a practical, cost-effective, and efficient ASL recognition solution adaptable for educational, social, and healthcare settings.

Introduction

The text describes the development of a real-time American Sign Language (ASL) recognition system aimed at bridging the communication gap between individuals who are Deaf or Hard of Hearing and those unfamiliar with ASL. The system leverages deep learning and computer vision to recognize static ASL alphabet gestures via webcam input, providing an affordable, user-friendly solution for real-time communication.

Key Points:

1. Communication Barrier: The need for effective communication tools for the Deaf and Hard of Hearing community is essential, as most people are not proficient in ASL. This leads to challenges in areas like education, healthcare, and public services.

2. Technological Solution: The project focuses on creating a lightweight, real-time ASL recognition system using a Convolutional Neural Network (CNN) model. The system captures hand gestures using a webcam and translates them into text. By leveraging YCrCb color space segmentation, the system effectively isolates the hand region, improving accuracy under varying environmental conditions.

3. Dataset & Model: A custom dataset of ASL alphabet gestures is used for training. The CNN model, optimized for low-cost devices, achieved 96.3% accuracy. The system is designed to run efficiently on low-end hardware, making it accessible for a broad range of users.

4. Real-Time & Low-Cost Advantage: Unlike traditional systems that require expensive hardware (e.g., gloves or depth cameras), this vision-based system offers a cost-effective and non-intrusive solution. It can be deployed in diverse environments, from education to public service kiosks.

5. Challenges: The system faces challenges in accurate segmentation, handling complex backgrounds, and varying lighting conditions. Misclassifications occur with gestures that have similar visual appearances, such as “M” vs. “N” or “U” vs. “V”. However, the system uses techniques like sliding window averaging to stabilize predictions and minimize errors.

6. Experimental Results:

   • The model was trained for 50 epochs, achieving a training accuracy of 98.6% and a validation accuracy of 96.3%.

   • In real-time testing, the model maintained reasonable responsiveness (~20 FPS) and handled variations in lighting conditions effectively.

7. System Integration: The system is designed for integration into various applications such as mobile apps, healthcare interfaces, and assistive technologies, thus enhancing the inclusivity of communication for the Deaf and Hard of Hearing community.

8. Future Improvements: Despite the system's strong performance, improvements can be made in distinguishing ambiguous hand gestures and real-time usage. Additional techniques, like hand landmark detection, could further enhance gesture recognition and system robustness.

Conclusion

This study presents a successful design and implementation of a real-time American Sign Language (ASL) recognition system capable of classifying static hand gestures corresponding to the 26 English alphabets. The system utilizes a lightweight Convolutional Neural Network (CNN) trained on a custom dataset composed of grayscale images, ensuring efficient learning of segmented hand shapes. By employing YCrCb-based image preprocessing, the system enhances the clarity of hand contours while reducing background noise, which significantly improves classification accuracy. The real-time integration with a standard webcam feed and the implementation of a prediction smoothing mechanism using a deque buffer allowed for more stable gesture recognition, even in the presence of motion blur or noise. One of the standout features of the system is its portability and affordability, as it does not rely on any specialized hardware such as gloves, depth sensors, or infrared cameras. It achieves real-time performance with frame rates between 24–27 FPS and latency under 100 milliseconds, making it suitable for practical deployment. Overall, this work offers an accurate, accessible, and cost-effective solution for ASL recognition and paves the way for future extensions to dynamic gesture recognition and full-sentence interpretation.

References

[1] Ahmed, S. Rahman, and T. Ali, “Real-Time ASL Alphabet Recognition Using MobileNetV2,” Journal of Image Processing and AI, vol. 7, no. 2, pp. 45–56, 2025. [2] Y. Zhang, M. Lin, and J. Zhao, “A Deep Learning Framework for ASL Gesture Recognition Using Webcam-Based Inputs,” IEEE Transactions on Multimedia, vol. 26, no. 1, pp. 88–99, 2024. [3] R. Kumar and P. Singh, “Webcam-Based American Sign Language Recognition Using CNN,” International Journal of Computer Vision & AI, vol. 11, no. 3, pp. 110–120, 2023. [4] X. Li, H. Chen, and Y. Wang, “Attention-Based CNN for ASL Alphabet Classification,” Pattern Recognition Letters, vol. 163, pp. 12–20, 2022. [5] D. Sharma and A. Patel, “Real-Time ASL Detection Using CNN and OpenCV,” International Journal of Emerging Technologies in Learning (iJET), vol. 17, no. 5, pp. 78–86, 2022. [6] T. Nguyen, V. Ho, and L. Tran, “Transfer Learning-Based Sign Language Recognition System,” Computer Vision and Image Understanding, vol. 212, pp. 103–114, 2021. [7] M. Hussain, A. Qureshi, and N. Farooq, “Gesture-Based Communication Aid Using CNN and Real-Time Hand Tracking,” Sensors and Actuators A: Physical, vol. 329, pp. 112–122, 2021. [8] P. Mehta and R. Kapoor, “Skin Segmentation Techniques for Real-Time Hand Gesture Detection,” Journal of Real-Time Image Processing, vol. 18, no. 4, pp. 755–766, 2021. [9] J. Gonzalez, T. Reyes, and K. Moreno, “ASL Alphabet Recognition with Data Augmentation,” Journal of Machine Learning Research, vol. 21, no. 6, pp. 345–356, 2020. [10] K. Rao and R. Iyer, “Low-Cost ASL Recognition Using Python and TensorFlow,” International Journal of Artificial Intelligence Research, vol. 14, no. 2, pp. 201–209, 2020. [11] J. J. Raval and R. Gajjar, \"Real-time Sign Language Recognition using Computer Vision,\" in *Proc. Int. Conf. on Smart and Sustainable Computing (ICPSC)*, 2021. [12] S. Bankar, T. Kadam, V. Korhale and A. A. Kulkarni, \"Real Time Sign Language Recognition Using Deep Learning,\" *Int. Res. J. Eng. Technol. (IRJET)*, vol. 9, no. 4, pp. 1234–1240, 2022. [13] Z. Yang, Z. Shi, X. Shen and Y.-W. Tai, \"SFNet: Structured Feature Network for Continuous Sign Language Recognition,\" *arXiv preprint arXiv:1908.01341*, 2019. [14] A. S. Nikam and A. G. Ambekar, \"Sign Language Recognition Using Image Based Hand Gesture Recognition Techniques,\" in *Online Int. Conf. on Green Engineering and Technologies (ICGET)*, 2016, doi: 10.1109/GET.2016.7916786. [15] G. Rajesh, X. M. Raajini, K. M. Sagayam and H. Dang, \"A statistical approach for high order epistasis interaction detection for prediction of diabetic macular edema,\" *Informatics in Medicine Unlocked*, vol. 20, p. 100362, 2020. [16] S. Daniels, N. Suciati and C. Fatichah, \"Indonesian Sign Language Recognition using YOLO Method,\" *IOP Conf. Ser.: Mater. Sci. Eng.*, vol. 1077, p. 012029, 2021, doi: 10.1088/1757-899X/1077/1/012029. [17] A. Mujahid, M. Awan, A. Yasin, M. Mohammed, R. Damasevicius, R. Maskeliunas and K. Hameed, \"Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model,\" *Applied Sciences*, vol. 11, no. 9, p. 4164, 2021, doi: 10.3390/app11094164. [18] About.almentor.net, \"The Deaf And Mute – Almentor.Net,\" 2020. [Online]. Available: https://about.almentor.net.

Copyright

Copyright © 2025 Ms. Pranita Chandramuni Sawant. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.