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Journal articles on the topic 'Indian Sign Language Recognition'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Malge, Manasi, Vidhi Deshmukh, and Harshwardhan Kharpate. "Indian Sign Language Recognition." International Journal of Science and Research (IJSR) 11, no. 3 (2022): 1164–70. http://dx.doi.org/10.21275/sr22325125614.

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M, Vaishnavi. "Indian Sign Language Recognition." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47426.

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Abstract - Indian Sign Language Recognition System provides a robust solution towards real-time sign language recognition, particularly for the deaf and hard-of-hearing. The system compared to traditional approaches relying more on vision-based features and less on contextual awareness utilizes deep generative models and transfer learning techniques to provide improved accuracy. The constructed approach enunciates the challenge of temporal boundary localizations among continuous gestures as weak supervised learning, where boundaries among continuous gestures are indeterminate. As a counter mea

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Singh Karki, Abhishek. "Sign Language Recognition System Sign Wave." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47343.

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Abstract – People with hearing and speech disabilities often struggle to communicate without a translator, as sign language is not universally understood. They rely heavily on hand gestures for non-verbal communication. To address this, the paper proposes a system for automatic recognition of finger spelling in Indian Sign Language. The process begins by capturing the sign as an image input. Skin colour-based segmentation is used to detect hand shape, followed by conversion to a binary image. A Euclidean distance transformation is applied, and row/column projections are performed. For feature

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M. Mullur, Kumara Maruthi. "Indian Sign Language Recognition System." International Journal of Engineering Trends and Technology 21, no. 9 (2015): 450–54. http://dx.doi.org/10.14445/22315381/ijett-v21p288.

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Rokade, Yogeshwar I., and Prashant M. Jadav. "Indian Sign Language Recognition System." International Journal of Engineering and Technology 9, no. 3S (2017): 189–96. http://dx.doi.org/10.21817/ijet/2017/v9i3/170903s030.

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Mishra, Ravita, Gargi Angne, Nidhi Gawde, Preeti Khamkar, and Sneha Utekar. "SignSpeak: Indian Sign Language Recognition with ML Precision." Indian Journal Of Science And Technology 18, no. 8 (2025): 620–34. https://doi.org/10.17485/ijst/v18i8.4049.

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Objectives: To develop an accessible educational platform for Indian Sign Language (ISL) recognition, bridging communication gaps using advanced machine learning techniques, and promoting inclusivity for the hearing-impaired community. Methods: The study utilized Random Forest for classifying ISL letters and numbers with 1200 images per class and Long Short-Term Memory (LSTM)/Large Language Model (LLM) for gesture-based word and sentence recognition using 120 custom images. Feedback from Jhaveri Thanawala School for the Deaf validated the approach. Findings: The Random Forest model achieved 99

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Deshpande, Padmanabh D., and Sudhir S. Kanade. "Recognition of Indian Sign Language using SVM classifier." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1053–58. http://dx.doi.org/10.31142/ijtsrd11104.

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Shinde, Aditya. "Indian Sign Language Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41093.

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- The communication gap remains one of the most significant barriers between individuals with hearing and speech impairments and the broader society. This project addresses this challenge by developing a real-time Indian Sign Language (ISL) detection system that leverages computer vision and machine learning techniques. By capturing hand gestures from video input, the system translates these movements into text or speech, enabling effective communication between ISL users and those unfamiliar with the language. Additionally, the system incorporates text-to-speech functionality, ensuring a seam

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Musale, Sandeep, Kalyani Gargate, Vaishnavi Gulavani, Samruddhi Kadam, and Shweta Kothawade. "Indian sign language recognition and search results." Journal of Autonomous Intelligence 6, no. 3 (2023): 1000. http://dx.doi.org/10.32629/jai.v6i3.1000.

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<p>Sign language is a medium of communication for people with hearing and speaking impairment. It uses gestures to convey messages. The proposed system focuses on using sign language in search engines and helping specially-abled people get the information they are looking for. Here, we are using Marathi sign language. Translation systems for Indian sign languages are not much simple and popular as American sign language. Marathi language consists of words with individual letters formed of two letter = Swara + Vyanjan (Mulakshar). Every Vyanjan or Swara individually has a unique sign whic

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Shinde, Aditya. "Enhanced Indian Sign Language Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49874.

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Abstract - The communication problem involving members of society who have speech and hearing impairments is still not fully resolved. In an earlier study, we created a real-time Indian Sign Language (ISL) recognition system which uses LSTM architecture for sequential gesture recognition. The focus of this paper is on further improving this system by changing the architecture from LSTM to CNN to enhance spatial feature extraction and overall system performance. Using a more comprehensive ISL dataset, we trained and tested the model and added new advanced preprocessing techniques such as Gaussi

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Goyal, Er Kanika, and Amitoj Singh. "Indian Sign Language Recognition System for Differently-able People." Journal on Today's Ideas - Tomorrow's Technologies 2, no. 2 (2014): 145–51. http://dx.doi.org/10.15415/jotitt.2014.22011.

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Raheja, J. L., A. Mishra, and A. Chaudhary. "Indian sign language recognition using SVM." Pattern Recognition and Image Analysis 26, no. 2 (2016): 434–41. http://dx.doi.org/10.1134/s1054661816020164.

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Preeti Jain, Prof. "Healthcare Application Using Indian Sign Language." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50261.

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Abstract - The absence of standardized and easily available technology solutions for people with disabilities—especially those who use Indian Sign Language (ISL) to access vital services like healthcare—means that there are still significant communication hurdles in India. Unlike American Sign Language (ASL), which is mostly one-handed, ISL relies on intricate two-handed motions, which creates unique difficulties for software-based interpretation systems. The lack of extensive, standardized ISL datasets, which are essential for developing precise machine learning and gesture recognition models

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Rambhia, Jainam, Manan Doshi, Rashi Lodha, and Stevina Correia. "Real Time Indian Sign Language Recognition using Deep LSTM Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 1041–45. http://dx.doi.org/10.22214/ijraset.2023.48695.

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Abstract: On our planet, people with speech and hearing disabilities are part of society. Communication becomes difficult when it is necessary to interact with survivors and the general public. In some races, people with disabilities practice different sign languages for communication. For people with speech and hearing disabilities, sign language is a basic means of communication in everyday life. However, a large portion of our community is unaware of the sign languages they practice, so bringing them into the mainstream is an incredible challenge. Today, computer vision-based solutions are

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Patole, Piyush, Mihir Sarawate, and Krushna Joshi. "A Communication Translator Interface for Sign Language Interpretation." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4546–58. http://dx.doi.org/10.22214/ijraset.2023.52325.

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Abstract: Sign language is an essential means of communication for deaf and hard-of-hearing individuals. However, unlike spoken languages which have a universal language, every country has its own native sign language. In India, the Indian Sign Language (ISL) is used. This survey aims to provide an overview of the recognition and translation of essential Indian sign language. While significant research has been conducted in American Sign Language (ASL), the same cannot be said for Indian Sign Language due to its unique characteristics. The proposed method focuses on designing a tool for transl

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V.Nair, Anuja, and Bindu V. "A Review on Indian Sign Language Recognition." International Journal of Computer Applications 73, no. 22 (2013): 33–38. http://dx.doi.org/10.5120/13037-0260.

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Kaur, Sarbjeet, and V. K. Banga. "Boltay Hath for Indian Sign Language Recognition." International Journal of Applied Information Systems 7, no. 1 (2014): 1–7. http://dx.doi.org/10.5120/ijais14-451103.

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POOJA, MALHOTRA, K. MANIAR CHIRAG, V. SANKPAL NIKHIL, and R. THAKKAR HARDIK. "INDIAN SIGN LANGUAGE RECOGNITION SYSTEM USING OPENPOSE." i-manager's Journal on Computer Science 7, no. 2 (2019): 43. http://dx.doi.org/10.26634/jcom.7.2.15993.

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Rokade, Prajakta, Archana Kadam, Dipti Shinde, Shalini Yadav, and Neha Sali. "Indian Sign Language Recognition System in Marathi Language Text." International Journal of Computer Applications 182, no. 30 (2018): 19–22. http://dx.doi.org/10.5120/ijca2018918202.

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Rokade, Prajakta, Neha Sali, Dipti Shinde, and Shalini Yadav. "Indian Sign Language Recognition System in Marathi Language Text." International Journal of Computer Sciences and Engineering 7, no. 5 (2019): 881–85. http://dx.doi.org/10.26438/ijcse/v7i5.881885.

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21

Pradnya D. Bormane. "Indian Sign Language Recognition: Support Vector Machine Approach." Advances in Nonlinear Variational Inequalities 27, no. 3 (2024): 716–27. http://dx.doi.org/10.52783/anvi.v27.1438.

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Indian Sign Language (ISL) is the primary form of communication for the dumb and deaf community in India. Recognizing Indian Sign Language plays an imperative part in promoting communication rights, social inclusion and equality for deaf people, while also contributing to technological advancement and cultural diversity. System’s ability to automatically recognize ISL signs could significantly improve community interactions between deaf and people with hearing loss. The objective of this research is to design a system that can accurately recognize and interpret Indian Sign language (ISL), ther

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Poornima, B. V., and S. Srinath. "Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures." Indian Journal Of Science And Technology 17, no. 7 (2024): 660–69. http://dx.doi.org/10.17485/ijst/v17i7.2836.

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Objectives: The objective of this paper is to introduce and demonstrate an innovative approach for the recognition of Indian sign language gestures, with a focus on bridging communication gap between the deaf and hearing communities. The goal is to contribute to the development of effective tools and technologies that facilitate seamless communication between individuals using sign language and the people with no knowledge about sign language. Methods: The methodology consists of three key steps. First, data pre-processing involves resizing and contours extraction. Next, feature extraction emp

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Padmanabh, D. Deshpande, and S. Kanade Sudhir. "Recognition of Indian Sign Language using SVM classifier." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 1053–58. https://doi.org/10.31142/ijtsrd11104.

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Sign language is the medium of communication for the hearing impaired people. It uses gestures instead of sound to convey meaning. It combines hand shapes, orientation and movement of the hands, arms or body, facial expressions and lip patterns for conveying messages. Different types of project are done against deaf, mute, hard hearing people. A system with computer human interface is proposed for sign language recognition. But there is country wide variation available in that project. The main idea of this project is design a system which is useful for communication of that people with outsid

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D, Karthika Renuka, and Ashok Kumar L. "Indian Sign Language Recognition Using Deep Learning Techniques." International Journal of Computer Communication and Informatics 4, no. 1 (2022): 36–42. http://dx.doi.org/10.34256/ijcci2214.

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By automatically translating Indian sign language into English speech, a portable multimedia Indian sign language translation program can help the deaf and/or speaker connect with hearing people. It could act as a translator for those that do not understand sign language, eliminating the need for a mediator and allowing communication to take place in the speaker's native language. As a result, Deaf-Dumb people are denied regular educational opportunities. Uneducated Deaf-Dumb people have a difficult time communicating with members of their culture. We provide an incorporated Android applicatio

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Raut, Srushti, Panav Patel, Sohan Vichare, Gayatri Hegde, and Rahul Durvas. "Indian Sign Language Recognition System for Deaf and Dumb Using CNN." International Journal of Scientific Engineering and Research 11, no. 4 (2023): 34–37. https://doi.org/10.70729/se23416143344.

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Nalavade, Dr Kamini, Dr Pallavi Baviskar, Mayank Katiyara, Hitesh Paighan, Devendra Chaudhari, and Sanketgir Gosavi. "Multi Sign Language Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40539.

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The deaf and hard-of-hearing community often experiences communication barriers due to people who are not well-versed in the use of sign language.[1] This project would address this problem by developing an all-embracing machine learning (ML) model which would be able to interpret and translate the hand movements of American Sign Language (ASL) and Indian Sign Language (ISL) into corresponding spoken or written language in real time.[2] In using data from cameras, the system is designed to precisely predict and translate gestures both ASL and ISL. The solution integrates Natural Language Proce

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Patil, Rachana, Vivek Patil, Abhishek Bahuguna, and Gaurav Datkhile. "Indian Sign Language Recognition using Convolutional Neural Network." ITM Web of Conferences 40 (2021): 03004. http://dx.doi.org/10.1051/itmconf/20214003004.

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Communicating with the person having hearing disability is always a major challenge. The work presented in paper is an exertion(extension) towards examining the difficulties in classification of characters in Indian Sign Language(ISL). Sign language is not enough for communication of people with hearing ability or people with speech disability. The gestures made by the people with disability gets mixed or disordered for someone who has never learnt this language. Communication should be in both ways. In this paper, we introduce a Sign Language recognition using Indian Sign Language.The user mu

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Gaonkar, Niyati V., and Vishal R. Gori. "Real-Time Bidirectional Translation System Between Text and Indian Sign Language Using Deep Learning and NLP Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44150.

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In this paper, we present a real-time translation system that bridges the communication gap between the hearing and non-hearing communities. Our system converts English text to Indian Sign Language (ISL) and vice versa, using Natural Language Processing (NLP) techniques and deep learning-based gesture recognition. The system supports video-based gesture recognition for ISL and provides accurate text translations in real-time. This study addresses the technical challenges involved, including feature extraction from gestures and translating com- plex ISL sentences using neural networks like LSTM

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K, Sanjusaran, Shakthipriyan S, and Supreetraju RU. "A REAL TIME INDIAN SIGN LANGUAGE RECOGNITION USING TENSORFLOW." International Journal of Engineering Research and Sustainable Technologies (IJERST) 2, no. 4 (2024): 26–33. https://doi.org/10.63458/ijerst.v2i4.98.

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Communication is the exchange of information, ideas, or emotions, typically through spoken or written language. However, for individuals who are deaf or mute, traditional communication methods may not be effective. Instead, they rely on sign language—a visual form of communication using gestures and movements. Unfortunately, many people are unfamiliar with sign language, creating a barrier between those who use it and those who do not. Machine learning offers a promising solution to this challenge. By training a model to recognize and translate sign language gestures into spoken or written lan

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D, Sarawathi, Anushree M, Dishanth HR, Naidhile S, and Thushar N. "Kannada Sign Language Recognition Using Machine Learning." International Journal of Multidisciplinary Research and Growth Evaluation 6, no. 3 (2025): 326–30. https://doi.org/10.54660/.ijmrge.2025.6.3..326-330.

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For the global community of people who are hearing or speech handicapped, sign language is an essential communication tool. While languages like Tamil and Hindi have found their way into Indian technology, Kannada Sign Language (KSL) has not yet been extensively used in digital applications. In order to recognize Kannada sign language, this research suggests a revolutionary machine learning-based method that focuses on both letter and word detection. By gathering a unique dataset of more than 6,000 samples from 20 different participants, our project fills the gap and guarantees resilience agai

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Ravita, Mishra, Angne Gargi, Gawde Nidhi, Khamkar Preeti, and Utekar Sneha. "SignSpeak: Indian Sign Language Recognition with ML Precision." Indian Journal of Science and Technology 18, no. 8 (2025): 620–34. https://doi.org/10.17485/IJST/v18i8.4049.

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<strong>Objectives:</strong> To develop an accessible educational platform for Indian Sign Language (ISL) recognition, bridging communication gaps using advanced machine learning techniques, and promoting inclusivity for the hearing-impaired community. <strong>Methods:</strong> The study utilized Random Forest for classifying ISL letters and numbers with 1200 images per class and Long Short-Term Memory (LSTM)/Large Language Model (LLM) for gesture-based word and sentence recognition using 120 custom images. Feedback from Jhaveri Thanawala School for the Deaf validated the approa

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L, Sukanya, Tharun E, Anup Raj G, Shreyas Singh T, and Srinivas S. "Indian sign language recognition using convolution neural network." E3S Web of Conferences 391 (2023): 01058. http://dx.doi.org/10.1051/e3sconf/202339101058.

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The goal of the project is to create a machine learning model that can classify the numerous hand motions used in sign language fingerspelling. Communication with deaf and dumb persons is frequently difficult. A variety of hand, finger, and arm motions that assist the deaf and hard of hearing in communicating with others and vice versa. Classification machine learning algorithms are taught on a set of image data in this userindependent model, and testing is done on a completely other set of data. For some people with particular needs, sign language is their only means of communicating their th

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Nandhitha M, Vasundhara. "SIGNVISION – A SMART BIDIRECTIONAL SIGN LANGUAGE TRANSLATOR." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04464.

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Abstract: Sign Vision is all-in-one solution for transparent communication by providing instant two-way translation of sign and spoken language. It uses state of the art computer vision, deep learning, and speech processing technologies to understand gestures and convert them into text and speech. Comment It also translates speech into animated sign images. Sign Vision supports several Indian languages and with its accessible interface, it is a versatile tool for communication, useful in public spaces, schools and healthcare settings. Sign Vision consists of multiple integrated modules that ha

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Singha, Joyeeta, and Karen Das. "Recognition of Indian Sign Language in Live Video." International Journal of Computer Applications 70, no. 19 (2013): 17–22. http://dx.doi.org/10.5120/12174-7306.

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R B., Mr Girme. "Indian Sign Language Recognition System Using PCA Features." International Journal for Research in Applied Science and Engineering Technology V, no. X (2017): 1192–97. http://dx.doi.org/10.22214/ijraset.2017.10171.

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Sahoo, Ashok Kumar. "Indian Sign Language Recognition Using Machine Learning Techniques." Macromolecular Symposia 397, no. 1 (2021): 2000241. http://dx.doi.org/10.1002/masy.202000241.

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Poornima, B. V., S. Srinath, S. Rashmi, and R. Rakshitha. "Performance Evaluation of Feature Fusion Approaches for Indian Sign Language Recognition System." Indian Journal Of Science And Technology 16, no. 41 (2023): 3691–703. http://dx.doi.org/10.17485/ijst/v16i41.1767.

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Shikalgar, Prof S. A. "Automated Sign Language Interpretation." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 806–10. https://doi.org/10.22214/ijraset.2025.68336.

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Communication plays an essential role in human interaction, allowing individuals to express ideas and emotions. While spoken languages are widely used, individuals with hearing and speech impairments rely on sign language. However, the lack of widespread understanding of sign language creates communication barriers between them and the hearing community. This study presents a real-time Indian Sign Language (ISL) recognition system using the Media pipe framework and Long Short-Term Memory (LSTM) networks. The approach involves training an LSTM model to distinguish between different signs, utili

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Bhagyashri, Pagar, Shelar Rutuja, Sheelavant Soumya, and A. Utikar Avinash. "Deep Learning for Sign Language Recognition." International Journal of Innovative Science and Research Technology 8, no. 3 (2023): 271–81. https://doi.org/10.5281/zenodo.7747476.

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A sign language is a way of communicating by using the hands instead of spoken words. Sign language is used by deaf and dump people to communicate with other individuals. People who are speech-impaired and also some people who have autism spectrum disorder face problem while communicating with normal as they can converse using only sign language. So, it becomes difficult for other individuals to understand this sign language. Each country usually has its own native sign language. The Indian Sign Language recognition application proposed here aims at solving the communication problem between pe

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Dixit, Karishma, and Anand Singh Jalal. "A Vision-Based Approach for Indian Sign Language Recognition." International Journal of Computer Vision and Image Processing 2, no. 4 (2012): 25–36. http://dx.doi.org/10.4018/ijcvip.2012100103.

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The sign language is the essential communication method between the deaf and dumb people. In this paper, the authors present a vision based approach which efficiently recognize the signs of Indian Sign Language (ISL) and translate the accurate meaning of those recognized signs. A new feature vector is computed by fusing Hu invariant moment and structural shape descriptor to recognize sign. A multi-class Support Vector Machine (MSVM) is utilized for training and classifying signs of ISL. The performance of the algorithm is illustrated by simulations carried out on a dataset having 720 images. E

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Alaria, Satish Kumar, Ashish Raj, Vivek Sharma, and Vijay Kumar. "Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language using CNN." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (2022): 10–14. http://dx.doi.org/10.17762/ijritcc.v10i4.5556.

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Sign Language Recognition is a device or program to help deaf and mute people. However, communication has always been difficult for a person with verbal and physical disabilities. Sign language recognition communication between the average person and the disabled using this device easily communicates with people who cannot communicate with the average person, this program reduces the communication gap between people. In total, the world has a population of about 15 -20% of the deaf and mute population which is a clear indication of the need for a Sign Language Awareness Program. Different meth

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Muthu Mariappan H and Dr Gomathi V. "Indian Sign Language Recognition through Hybrid ConvNet-LSTM Networks." EMITTER International Journal of Engineering Technology 9, no. 1 (2021): 182–203. http://dx.doi.org/10.24003/emitter.v9i1.613.

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Dynamic hand gesture recognition is a challenging task of Human-Computer Interaction (HCI) and Computer Vision. The potential application areas of gesture recognition include sign language translation, video gaming, video surveillance, robotics, and gesture-controlled home appliances. In the proposed research, gesture recognition is applied to recognize sign language words from real-time videos. Classifying the actions from video sequences requires both spatial and temporal features. The proposed system handles the former by the Convolutional Neural Network (CNN), which is the core of several

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D., Anbarasan, Aravind R., and Alice K. "GRS - Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 1221–25. https://doi.org/10.31142/ijtsrd9638.

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Recognition languages are developed for the better communication of the challenged people. The recognition signs include the combination of various with hand gestures, movement, arms and facial expressions to convey the words thought. The languages used in sign are rich and complex as equal as to languages that are spoken. As the technological world is growing rapidly, the sign languages for human are made to recognised by systems in order to improve the accuracy and the multiply the various sign languages with newer forms. In order to improve the accuracy in detecting the input sign, a model

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Sinha, Prof Pragya. "Design and Development of Indian Sign Language Character Recognition System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–13. http://dx.doi.org/10.55041/ijsrem27773.

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The purpose of this study is to look into the challenges involved in categorizing Indian Sign Language (ISL) characters. While a lot of research has been done in the related field of American Sign Language (ASL), not as much has been done with ISL. Lack of standard datasets, obscured traits, and variance in language with geography are the key barriers that have hindered much ISL research. Our study aims to progress this field by collecting a dataset from a deaf school and applying various feature extraction techniques to extract useful information, which is then input into a range of supervise

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Tiwari, Rishin, Saloni Birthare, and Mr Mayank Lovanshi. "Audio to Sign Language Converter." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 206–11. http://dx.doi.org/10.22214/ijraset.2022.47271.

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Abstract: The hearing and speech disabled people have a communication problem with other people. It is hard for such individuals to express themselves since everyone is not familiar with the sign language. The aim of this paper is to design a system which is helpful for the people with hearing / speech disabilities and convert a voice in Indian sign language (ISL). The task of learning a sign language can be cumbersome for people so this paper proposes a solution to this problem using speech recognition and image processing. The Sign languages have developed a means of easy communication prima

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Mishra, Harshita, Mansi Sharma, Muskan Ali, and Shivani Chaudhary. "Audio to Indian Sign Language Translator." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3904–7. http://dx.doi.org/10.22214/ijraset.2022.43269.

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Abstract: This project's primary purpose is to bridge the gap between deaf and hearing persons, which will benefit those with hearing impairments who employ a simple and effective way of sign language. Sign language is a visual language used by the deaf community. It employs body language, hand gestures, and facial expressions. Indian Sign Language is one of the most significant and commonly utilised modes of communication for individuals with speech and hearing difficulties. This web application facilitates communication for deaf and speech-impaired individuals. The primary focus of these new

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Anbarasan, D., R. Aravind, and K. Alice. "GRS – Gesture based Recognition System for Indian Sign Language Recognition System for Deaf and Dumb People." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1221–25. http://dx.doi.org/10.31142/ijtsrd9638.

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Gandhe, Dakshesh, Pranay Mokar, Aniruddha Ramane, and Dr R. M. Chopade. "Sign Language Recognition for Real-time Communication." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 288–93. http://dx.doi.org/10.22214/ijraset.2024.61514.

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Abstract: Sign language is an essential communication tool for India's Deaf and Hard of Hearing people. This study introduces a novel approach for recognising and synthesising Indian Sign Language (ISL) using Long Short-Term Memory (LSTM) networks. LSTM, a kind of recurrent neural network (RNN), has demonstrated promising performance in sequential data processing. In this study, we leverage LSTM to develop a robust ISL recognition system, which can accurately interpret sign gestures in real-time. Additionally, we employ LSTM-based models for ISL synthesis, enabling the conversion of spoken lan

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Jagtap, Mr Amol, Gaurav Pagare, Anushka Sandbhor, Vivek Patil, and Sakshi Divate. "Indian Sign Language Recognition Using CNN Inception V3 - A Review." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1093–100. http://dx.doi.org/10.22214/ijraset.2024.56887.

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Abstract: This research paper explores the intersection of sign language recognition & deep learning, with a focus on the utilization of CNNs and the Inception V3 architecture. The study emphasizes how crucial proper translation and identification of sign language is to closing the communication gap that exists between the hearing and the Deaf populations. It discusses challenges such as limited datasets and ambiguity in sign language and outlines the potential for future advancements in accessibility and education. By combining the power of deep learning and culturally tailored datasets,

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Muthusamy, Prema, and Gomathi Pudupalayam Murugan. "Occlusion Resistant Spatio-Temporal Hybrid Cue Network for Indian Sign Language Recognition and Translation." Indian Journal Of Science And Technology 17, no. 44 (2024): 4590–99. https://doi.org/10.17485/ijst/v17i44.2225.

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Objective: To tackle the issues of occlusion in human skeleton extraction and simplify the pixel matching related to the human skeleton structure for efficient Indian Sign Language (ISL) recognition and translation. Methods: This paper presents Occlusion-Resistant STHCN (OSTHCN) to tackle the occlusion problem in human skeleton extraction for effective ISL recognition and translation. This model incorporates Skeleton Occupancy Likelihood Map estimation using B-Spline curves to enhance the skeleton extraction. Due to occlusions caused by fingers and hands, the extracted skeleton is composed of

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