Hand Gesture Text Recognition

Abstract

Achieving precision, quick processing, and adaptability across different surroundings remains a major challenge for gesture recognition. This work introduces a vision-based framework designed to translate static hand signs into text while keeping computational effort minimal. The framework employs OpenCV for capturing and preprocessing input, cvzone for hand tracking, and a CNN model for classification. Each captured gesture is standardized and mapped to a letter, enabling direct text conversion. The approach is particularly helpful for people with hearing or speech difficulties and also finds applications in robotics, education, and immersive technologies. Testing confirms strong accuracy with very low delay during live operation.

Introduction

Gesture recognition is a key tool for assisting people with hearing and speech impairments, offering an intuitive, contact-free alternative to traditional input devices like keyboards and mice. Recent advances in computer vision and machine learning enable real-time gesture recognition under varying conditions.

Problem Statement:
Current gesture recognition systems face challenges such as sensitivity to environmental changes (lighting, background), high hardware costs (sensor gloves), processing delays in deep learning models, and limited datasets that restrict support for diverse sign languages.

Existing Systems:

• Glove-based methods provide accuracy but are expensive and inconvenient.

• Vision-based classical techniques are efficient but less reliable in changing environments.

• Deep learning models improve accuracy but often lack adaptability and real-time performance.

Dataset:
A dataset of static hand gestures representing the 26 English alphabets (A-Z) was created using webcam images under various lighting and background conditions. Images were preprocessed (cropped, resized, normalized) and augmented for better model generalization. The dataset was split into training (70%), validation (15%), and testing (15%) sets. Limitations include only static, mostly single-hand gestures with limited variation in skin tone and hand size.

Methodology:
The system uses a standard webcam to capture real-time video frames. The cvzone HandDetector (based on Mediapipe) detects and segments the hand region robustly. The hand image is preprocessed to standardize input for a CNN classifier that maps the gesture to a corresponding alphabet character. The recognized text is displayed live on the video interface.

Mathematical Model:
The input image frame is processed to detect and crop the hand region, normalized, and classified by a CNN to predict the corresponding alphabet letter.

Experimental Setup:
Implemented on a laptop with a webcam, using Python libraries (OpenCV, TensorFlow/Keras). Performance metrics include recognition accuracy and real-time processing speed.

Conclusion

This work develops a cost-effective and practical hand gesture recognition framework that combines vision techniques with CNNs. By overcoming drawbacks of glove-based and traditional methods, the system achieves high recognition accuracy while responding in real time. The framework is suitable for assistive technologies and other human–computer interaction domains, with potential future extensions to dynamic gestures, multiple sign languages, and speech integration.

References

[1] M. Garg, D. Ghosh, and P. Pradhan, “Video transformer network for dynamic gesture recognition,” arXiv preprint, 2025. [2] K. Spathis, N. Kardaris, and P. Maragos, “Multi-task learning for action and gesture recognition,” arXiv preprint, 2025. [3] R. G. Raju, K. Sushmitha, M. Priyanka, et al., “Real-time hand gesture recognition using CNN,” Global Journal of Eng. Innovations, vol. 5, no. 2, pp. 44, 2025. [4] O.-J. Kwon, J. Kim, S. Jamil, et al., “Dynamic gesture recognition with MediaPipe and deep learning,” Electronics, vol. 13, no. 16, 2024. [5] M. Zhu, C. Zhang, J. Wang, et al., “CNN-TCN fusion for radar-based gesture recognition,” Sensors, vol. 23, no. 20, 2023. [6] S. Gnanapriya and K. Rahimunnisa, “Hybrid deep learning for real-time gesture recognition,” Intelligent Automation and Soft Computing, vol. 36, no. 1, pp. 1105–1119, 2023. [7] H. K. Vashisth, T. Tarafder, R. Aziz, et al., “Sign language gesture recognition using deep learning,” Engineering Proceedings, vol. 59, no. 1, 2023. [8] V. I. Pavlovic, R. Sharma, and T. S. Huang, “Visual interpretation of hand gestures,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 677–695, 1997 [9] Li, Y., et al. (2020). Deep learning-based sign language recognition: A review and new perspectives. Neurocomputing, 386, 99–116. [10] Ahmed, T., & Hossain, M. (2021). Deep CNN for robust static hand gesture recognition. Journal of Artificial Intelligence and Soft Computing Research, 11(3), 213–225.

Copyright

Copyright © 2025 Mr. Sanjay Arya S, Dr. Girish Kumar D. 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.