Innovative Hand Sign Recognition to Text-and- Speech Conversion System

Abstract

The rapid advancements in human-computer interaction havepavedthewayforinnovativeassistivetechnologies. Thisproject,\"Hand-SigntoText-and-SpeechConversion System,\" focuses on developing a real-time hand sign recognition system that translates gestures into text and speech. Utilizing computer vision and machine learning techniques, particularly Convolutional Neural Networks (CNNs), the system aims to bridge the communication gapforindividualswithspeechandhearingimpairments. The project employs OpenCV, MediaPipe, and TensorFlow for hand sign detection and recognition, ensuring high accuracy and real-time processing. The study also explores the challenges, including gesture variability and dataset limitations, while proposing solutions for enhancing system robustness.

Introduction

Importance of Accessibility in Technology:
Ensuring technology is accessible to everyone, especially people with disabilities, is vital. For individuals with hearing and speech impairments, communication barriers exist that specialized tools like hand-sign recognition technology can help overcome.

Hand-Sign Recognition as a Solution:
This technology translates hand gestures into readable text or speech using Machine Learning (ML) and Computer Vision. ML trains models on large gesture datasets, while computer vision detects and interprets hand movements.

Real-Time Gesture Recognition:
Advanced ML models enable real-time detection, classification, and conversion of gestures into text or speech, facilitating seamless communication.

Applications:
Beyond assistive communication, hand-sign recognition is useful in Virtual Reality, smart device control, education (teaching sign language), and healthcare for better patient communication.

Literature Survey:

• CNNs (Convolutional Neural Networks) offer high accuracy in gesture recognition.

• Earlier methods like Hidden Markov Models (HMMs) face real-time limitations.

• Hybrid approaches combining SVM (Support Vector Machines) and ANN improve classification.

• Feature extraction techniques such as SURF and K-means clustering optimize recognition.

• Diverse datasets and real-time processing remain key challenges in sign language recognition research.

Proposed System Methodology:

• Data collected from public sources and custom images.

• Preprocessing includes image segmentation and noise removal.

• Features extracted using SURF and K-means clustering.

• Gesture classification done via CNN and refined by SVM.

• Recognized gestures converted to text and speech using Google’s TTS API.

Model Training:

• Uses TensorFlow/Keras with data augmentation to improve performance.

• Hyperparameter tuning applied to optimize accuracy.

• SVM adds robustness to CNN predictions.

Results:

• Achieved around 90% accuracy on diverse datasets.

• Real-time recognition with minimal delay.

• High precision (88%), recall (91%), and F1-score (89%).

• Outperformed traditional HMM and basic ANN models.

• Effective across various hand shapes but less so in low-light conditions.

Conclusion

Thisprojectpresentsapracticalapproachtohand- sign recognition, providing a valuable tool for individuals withspeechorhearingimpairments.ByintegratingCNNs, featureextractionmethods,andtext-to-speechtechnology, the system ensures real-time, high-accuracy gesture recognition.However,challengesremain,includingdataset limitations and environmental factors affecting accuracy. Future improvements will focus on expanding dataset diversity and refining real-time performance.

References

[1] Zhang, Z., et al. (2019). \"Hand Gesture Recognition Using Convolutional Neural Networks.\" OpenCV documentation. [2] MediaPipeHands:On-deviceReal-timeHand Tracking. https://google.github.io/mediapipe/solutions/hands [3] DeepConvolutionalNetworkwithLongShort-Term Memory Layers for Dynamic Gesture Recognition,Rostyslav Siriak, Inna Skarga-Bandurova, YehorBoltov, IEEE 2019. [4] Wenwen Yang, Jinxu Tao, Changfeng Xi, ZhongfuYe,“SignLanguageRecognitionSystem Basedon WeightedHiddenMarkovModel”,IEEE 2015. [5] Zhen Zhang, Ziyi Su, GeYang, “Real-Time Chinese Sign Language Recognition Based on Artificial Neural Networks”, IEEE 2019. [6] Sandrine Tornay, MarziehRazavi, Mathew Magimai.-Doss, “Towards multilingual sign language recognition”, IEEE 2020. [7] DikshaHatibaruah, Anjan Kumar Talukdar, KandarpaKumarSarma, “A Static Hand Gesture Based Sign Language Recognition System using Convolutional Neural Networks”, IEEE 2020. [8] MuthuMariappan H, DrGomathi V, “Real-Time Recognition of Indian Sign Language”, IEEE 2019. [9] JuhiEkbote, Mahasweta Joshi, “Indian Sign Language Recognition Using ANN and SVM Classifiers”, IEEE 2018. [10] JinaleeJayeshkumarRaval,RuchiGajjar,“Real- time Sign Language Recognition using Computer Vision”, IEEE 2021. [11] Nan Song, Hongwu Yang, PengpengZhi, “Towards Realizing Sign Language to Emotional Speech Conversion by Deep Learning”, IEEE 2021. [12] RajitNair,DileepKumarSingh,Ashu,ShivamYadav, SourabhBakshi, “Hand Gesture Recognition system for physically challenged people using IoT”, IEEE 2021. [13] Muneer Al-Hammadi, Ghulam Muhammad, Wadood Abdul, Mansour Alsulaiman, Mohammed A. Bencherif, Tareq S. Alrayes, “Deep Learning-Based Approach for Sign Language Gesture Recognition With Efficient Hand Gesture Representation”, IEEE 2020. [14] Jagtap, B., &Dhotre, V.A. (2014). “SVM and HMM Based Hybrid Approach of Sentiment Analysis for Teacher Feedback Assessment”.

Copyright

Copyright © 2025 Prof. Swapna Tikore , Atharva Bhosale, Gaurav Nikalje, Kaushal Chawale, Nikhil Patil. 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.