Sign Language Recognition System

Abstract

This comprehensive review explores the evolving landscape of gesture and emotion recognition technologies, with a focus on applications for the deaf and hard of hearing communities. The study introduces an efficient deep convolutional neural network approach for hand gesture recognition, leveraging transfer learning to overcome dataset limitations. Evaluation on three diverse datasets demonstrates high recognition rates, emphasizing the system\'s potential in sign language analysis. Emotion recognition systems, crucial for humancomputer interaction, are investigated, comparing contact-less methods like facial analysis with physiological parameter monitoring through smart wearables. The incorporation of multimodal emotional computing is investigated, exhibiting different modalities\' accuracy. Additionally, the paper delves into technological advancements in sign language recognition, visualization, and synthesis, identifying trends and gaps. The review concludes with a proposed framework for sign language recognition research, acknowledging the importance of diverse input modalities and anticipating future developments in this dynamic field.

Introduction

American Sign Language (ASL) is widely used by deaf and mute individuals as their primary mode of communication since they cannot rely on spoken language. It uses hand gestures and visual symbols to express thoughts, which can be understood visually. The project focuses on building a system that recognizes fingerspelling-based ASL gestures and combines them to form complete words, helping bridge communication between deaf-mute individuals and people who do not understand sign language.

The main objective is to develop a user-friendly human-computer interface using Convolutional Neural Networks (CNNs) that can identify hand gestures from images and convert them into text and speech. This would improve accessibility and inclusion for over 70 million sign language users worldwide by enabling easier communication in daily life, education, and services.

The literature review highlights that sign language recognition is an important application of computer vision and deep learning. CNNs and 3D-CNNs are commonly used for gesture recognition, but challenges such as limited labeled datasets exist. Techniques like transfer learning and spatiotemporal feature extraction help improve accuracy and robustness.

The proposed system works in multiple stages: capturing images, preprocessing, detecting and tracking the hand, extracting features, classifying gestures using a trained model, and converting the output into text and speech. The system uses tools like Python, OpenCV, TensorFlow, Keras, and MediaPipe, along with a webcam for input. Overall, the project aims to create an effective and accessible communication tool for both deaf-mute users and others.

Conclusion

In conclusion, this comprehensive review navigates the intricate landscape of gesture and emotion recognition technologies, particularly focusing on their applications for the deaf and hard of hearing communities. For gesture identification, this paper presents an effective deep convolutional neural network (CNN) method that emphasizes transfer learning to overcome dataset constraints and achieve high recognition rates across a variety of datasets. In the context of sign language analysis, the system\'s importance is emphasized, highlighting its ability to close communication gaps. Essentially, this study offers a comprehensive perspective on the state of gesture and emotion identification technologies, illuminating their uses, developments, and prospects. The proposed framework and insights into SLR and gesture recognition methodologies contribute to the ongoing dialogue in these dynamic fields. The interdisciplinary nature of the study, encompassing computer vision, machine learning, and human computer interaction, underscores its significance in advancing inclusive technologies for diverse user communities.

References

[1] Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O\'Reilly Media. [2] S. Kumar, et al. (2020). Hand Gesture Recognition Using Deep Learning Techniques. International Journal of Computer Applications, 176(14), 25–31. [3] Kaur, A., & Singh, S. (2021). Vision-Based Sign Language Recognition Using Machine Learning. International Journal of Advanced Research in Computer Science, 12(3), 88–94. 4. R. [4] Sharma, et al. (2019). Real-Time Sign Language Recognition System Using CNN. International Journal of Engineering Research & Technology (IJERT), 8(4), 45–52. [5] S. Mitra & T. Acharya (2007). Gesture Recognition: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(3), 311–324. [6] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. [7] A Sign Language Recognition using Improved Grey Wolf Optimization based Neural Networks” – Journal of Al-Qadisiyah, 2024 [8] Sign Language Recognition: A Comprehensive Review of Traditional and Deep Learning Approaches, Datasets, and Challenges” – IEEE Access, 2024 [9] “SLR-YOLO: An Improved YOLOv8 Network for Real-Time Sign Language Recognition” – Journal of Intelligent & Fuzzy Systems, 2024 [10] “Sign Language Recognition Using Convolutional Neural Network” – International Journal of Intelligent Systems and Applications in Engineering, 2024

Copyright

Copyright © 2026 Gagandeep Ahalawat, Ashish Kumar, Aayushi , Aayushi Chokkar , Dr. K.S. Mishra . 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.