International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
Indian Sign Language Recognition using Full Body Pose Estimation Sandesh Dandge1, Pranav Indore2, Vinayak Vairat3, Mahesh Wagh4, R. B. Murumkar5 1Department of Information Technology, SCTR's PICT Dhankawadi, Pune
2 Department of Information Technology, SCTR's PICT Dhankawadi, Pune
Department of Information Technology, SCTR's PICT Dhankawadi, Pune Department of Information Technology, SCTR's PICT Dhankawadi, Pune 5 Assistant Professor, Department of Information Technology, SCTR's PICT Dhankawadi, Pune ---------------------------------------------------------------------***--------------------------------------------------------------------3 4
Abstract - This survey paper presents a comprehensive
This survey paper aims to synthesize findings from seven recent research papers focused on developing and evaluating ISL detection systems. We explore methodologies employed in recognizing both static signs, which involve fixed hand shapes, and dynamic gestures that convey meaning through movement. By examining the strengths and limitations of various machine learning models---including CNNs, LSTMs, SVMs, Inception models, RNNs, HMMs, and Random Forest classifiers---we provide insights into the effectiveness of different approaches in accurately recognizing sign language gestures.
analysis of recent advancements in Indian Sign Language (ISL) detection systems, emphasizing the role of various machine learning techniques for both static and gesture-based sign recognition. It investigates the performance of multiple model architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), Inception models, Recurrent Neural Networks (RNN), Hidden Markov Models (HMM), and Random Forest classifiers, in accurately classifying sign gestures from video data. The study highlights preprocessing methodologies such as key point extraction and dataset preparation to enhance model training effectiveness. Additionally, the paper discusses the evaluation metrics employed to measure model performance and underscores the importance of selecting models tailored to the unique characteristics of ISL gestures. The findings highlight the necessity for improved recognition systems that are culturally relevant and capable of accommodating the distinctive features of Indian Sign Language, ultimately paving the way for more accessible communication solutions for the Deaf and Hard-of-Hearing (DHH) community in India.
Moreover, this paper highlights essential preprocessing techniques for preparing datasets for model training and emphasizes the importance of evaluating model performance through various metrics. Through this comprehensive review, we aim to underscore the significance of developing culturally relevant and accessible technology that bridges communication gaps for the DHH community in India. Our research not only contributes to the existing body of knowledge in the field but also paves the way for future advancements prioritizing inclusivity and accessibility in technology for all individuals.
Key Words: Indian Sign Language, Gesture Recognition, Machine Learning, Convolutional Neural Networks, Long Short-Term Memory, Random Forest, Hidden Markov Models.
2. LITERATURE SURVEY 2.1 Indian Sign Language Recognition Using Random Forest Classifier [1]
1. INTRODUCTION
This paper introduces a system to bridge the communication gap for speech-impaired individuals by recognizing Indian Sign Language (ISL) gestures. The system uses sensor equipped gloves integrated with various sensors (flex sensors, IMU, touch sensors) to capture hand gestures. The machine learning algorithm, specifically a Random Forest classifier, processes this data to recognize gestures and convert them into speech through a mobile app.
Communication is a fundamental human right, yet individuals within the Deaf and Hard-of-Hearing (DHH) community often face significant barriers in accessing and utilizing technology tailored to their needs. Indian Sign Language (ISL) serves as the primary mode of communication for millions in India, yet existing technological solutions frequently overlook the nuances inherent in this language. Recent advancements in machine learning have opened new avenues for enhancing ISL recognition systems, facilitating more effective communication between the DHH community and broader society.
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Models/Requirements: Hardware: Arduino Nano microcontrollers, RF, Bluetooth modules, Flex sensors, and IMU sensors. Machine Learning Model: Random Forest Classifier.
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