Relevant bibliographies by topics / Indian Sign Language Recognition

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  1. Journal articles
  2. Dissertations / Theses
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  5. Conference papers

Academic literature on the topic 'Indian Sign Language Recognition'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Indian Sign Language Recognition"

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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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Dissertations / Theses on the topic "Indian Sign Language Recognition"

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Mudduluru, Sravani. "Indian Sign Language Numbers Recognition using Intel RealSense Camera." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1815.

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The use of gesture based interaction with devices has been a significant area of research in the field of computer science since many years. The main idea of these kind of interactions is to ease the user experience by providing high degree of freedom and provide more interactive way of communication with the technology in a natural way. The significant areas of applications of gesture recognition are in video gaming, human computer interaction, virtual reality, smart home appliances, medical systems, robotics and several others. With the availability of the devices such as Kinect, Leap Motion
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Nel, Warren. "An integrated sign language recognition system." Thesis, University of Western Cape, 2014. http://hdl.handle.net/11394/3584.

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Doctor Educationis<br>Research has shown that five parameters are required to recognize any sign language gesture: hand shape, location, orientation and motion, as well as facial expressions. The South African Sign Language (SASL) research group at the University of the Western Cape has created systems to recognize Sign Language gestures using single parameters. Using a single parameter can cause ambiguities in the recognition of signs that are similarly signed resulting in a restriction of the possible vocabulary size. This research pioneers work at the group towards combining multiple
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Zafrulla, Zahoor. "Automatic recognition of American sign language classifiers." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53461.

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Automatically recognizing classifier-based grammatical structures of American Sign Language (ASL) is a challenging problem. Classifiers in ASL utilize surrogate hand shapes for people or "classes" of objects and provide information about their location, movement and appearance. In the past researchers have focused on recognition of finger spelling, isolated signs, facial expressions and interrogative words like WH-questions (e.g. Who, What, Where, and When). Challenging problems such as recognition of ASL sentences and classifier-based grammatical structures remain relatively unexplored in the
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Nayak, Sunita. "Representation and learning for sign language recognition." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002362.

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Nurena-Jara, Roberto, Cristopher Ramos-Carrion, and Pedro Shiguihara-Juarez. "Data collection of 3D spatial features of gestures from static peruvian sign language alphabet for sign language recognition." Institute of Electrical and Electronics Engineers Inc, 2020. http://hdl.handle.net/10757/656634.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Peruvian Sign Language Recognition (PSL) is approached as a classification problem. Previous work has employed 2D features from the position of hands to tackle this problem. In this paper, we propose a method to construct a dataset consisting of 3D spatial positions of static gestures from the PSL alphabet, using the HTC Vive device and a well-known technique to extract 21 keypoints from the hand to obtain a feature vector. A dataset of 35, 400
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Cooper, H. M. "Sign language recognition : generalising to more complex corpora." Thesis, University of Surrey, 2010. http://epubs.surrey.ac.uk/843617/.

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The aim of this thesis is to find new approaches to Sign Language Recognition (SLR) which are suited to working with the Limited corpora currently available. Data available for SLR is of limited quality; low resolution and frame rates make the task of recognition even more complex. The content is rarely natural, concentrating on isolated signs and filmed under laboratory conditions. In addition, the amount of accurately labelled data is minimal. To this end, several contributions are made: Tracking the hands is eschewed in favour of detection based techniques more robust to noise; for both sig
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Belissen, Valentin. "From Sign Recognition to Automatic Sign Language Understanding : Addressing the Non-Conventionalized Units." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG064.

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Les langues des signes (LS) se sont développées naturellement au sein des communautés de Sourds. Ne disposant pas de forme écrite, ce sont des langues orales, utilisant les canaux gestuel pour l’expression et visuel pour la réception. Ces langues peu dotées ne font pas l'objet d'un large consensus au niveau de leur description linguistique. Elles intègrent des signes lexicaux, c’est-à-dire des unités conventionnalisées du langage dont la forme est supposée arbitraire, mais aussi – et à la différence des langues vocales, si on ne considère pas la gestualité co-verbale – des structures iconiques
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Li, Pei. "Hand shape estimation for South African sign language." Thesis, University of the Western Cape, 2012. http://hdl.handle.net/11394/4374.

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>Magister Scientiae - MSc<br>Hand shape recognition is a pivotal part of any system that attempts to implement Sign Language recognition. This thesis presents a novel system which recognises hand shapes from a single camera view in 2D. By mapping the recognised hand shape from 2D to 3D,it is possible to obtain 3D co-ordinates for each of the joints within the hand using the kinematics embedded in a 3D hand avatar and smooth the transformation in 3D space between any given hand shapes. The novelty in this system is that it does not require a hand pose to be recognised at every frame, but rather
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Rupe, Jonathan C. "Vision-based hand shape identification for sign language recognition /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/940.

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Yin, Pei. "Segmental discriminative analysis for American Sign Language recognition and verification." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33939.

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This dissertation presents segmental discriminative analysis techniques for American Sign Language (ASL) recognition and verification. ASL recognition is a sequence classification problem. One of the most successful techniques for recognizing ASL is the hidden Markov model (HMM) and its variants. This dissertation addresses two problems in sign recognition by HMMs. The first is discriminative feature selection for temporally-correlated data. Temporal correlation in sequences often causes difficulties in feature selection. To mitigate this problem, this dissertation proposes segmentally-boosted
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Books on the topic "Indian Sign Language Recognition"

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People's Linguistic Survey of India, ed. Indian sign language(s). Orient Blackswan Private Limited, 2014.

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Liptak, Karen. North American Indian sign language. F. Watts, 1992.

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professeur, DuBois Daniel, ed. Indian signals and sign language. Bonanza Books, 1985.

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Liptak, Karen. North American Indian sign language. Scholastic, 1995.

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Fronval, George. Indian signals and sign language. Wings Books, 1994.

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Liptak, Karen. North American Indian sign language. Scholastic, 1995.

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Liptak, Karen. North American Indian sign language. F. Watts, 1990.

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Olsen, Madeline. Native American sign language. Scholastic, 2005.

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Association, British Deaf, ed. BSL - Britain's fourth language: The case for official recognition for British sign language. British Deaf Association, 1987.

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Museum of the Plains Indian and Crafts Center (U.S.), ed. Plains Indian sign language: A memorial to the Conference, September 4-6, 1930, Browning, Montana. U.S. Dept. of the Interior, Indian Arts and Crafts Board, Museum of the Plains Indian and Crafts Center, 1997.

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Book chapters on the topic "Indian Sign Language Recognition"

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Mishra, Vidyanand, Jayant Uppal, Honey Srivastav, Divyanshi Agarwal, and Harshit. "Sign Language Recognition for Indian Sign Language." In Advances in Data-Driven Computing and Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3250-4_12.

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Santhosh, K. S., Sanjay K. Hoysala, D. R. Srihari, Suhith Shekar Chandra, and A. N. Krishna. "Gesture Recognition of Indian Sign Language." In Lecture Notes in Networks and Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0980-0_3.

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Puri, Sudaksh, Meghna Sinha, Sanjana Golaya, and Ashwani Kumar Dubey. "Indian Sign Language Recognition Using Python." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4367-2_41.

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Mehrotra, Kapil, Atul Godbole, and Swapnil Belhe. "Indian Sign Language Recognition Using Kinect Sensor." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20801-5_59.

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Naren, J., R. Venkatesan, P. Rajendran, Galla Sai Vasudha, and Vivek. "Indian Sign Language Spelling Finger Recognition System." In Smart Systems and IoT: Innovations in Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8406-6_79.

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Bormane, Pradnya D., and S. D. Shirbahadurkar. "Indian Sign Language Recognition: A Comparative Study." In Intelligent Computing and Networking. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3177-4_13.

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Hore, Sirshendu, Sankhadeep Chatterjee, V. Santhi, et al. "Indian Sign Language Recognition Using Optimized Neural Networks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38771-0_54.

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Patel, Dhaval U., and Jay M. Joshi. "Review of Indian Dynamic Sign Language Recognition System." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_2.

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Rastogi, Umang, Anand Pandey, and Vinesh Kumar. "Recognition of indian sign language using hand gestures." In Artificial Intelligence, Blockchain, Computing and Security Volume 1. CRC Press, 2023. http://dx.doi.org/10.1201/9781003393580-23.

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Itkarkar Rajeshri, R., Anil Kumar V. Nandi, and Vaishali B. Mungurwadi. "Indian Sign Language Recognition Using Combined Feature Extraction." In Lecture Notes in Bioengineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6915-3_1.

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Conference papers on the topic "Indian Sign Language Recognition"

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Siddharth, S., R. Jayashree, and K. N. Shwetha. "Indian Sign Language Recognition System." In 2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2024. https://doi.org/10.1109/iscmi63661.2024.10851588.

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Kumar, Sujay Grama Suresh, and Jad Abbass. "Enhancing Sign Language Communication: Advanced Gesture Recognition Models for Indian Sign Language." In 2025 International Research Conference on Smart Computing and Systems Engineering (SCSE). IEEE, 2025. https://doi.org/10.1109/scse65633.2025.11031017.

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M, Ahmed Aslam, Mohamed Aabidh A, and N. Sabiyath Fatima. "Real-Time Indian Sign Language Recognition & Multilingual Sign Generation." In 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2025. https://doi.org/10.1109/icaiss61471.2025.11041851.

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Ojaswee, R. Sreemathy, Mousami Turuk, Jayashree Jagdale, and Mohammad Anish. "Indian Sign Language Recognition Using Video Vision Transformer." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10774678.

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Malviya, Shashi, Anita Mahajan, and Kamal Kumar Sethi. "A Comprehensive Study of Gesture Recognition in Indian Sign Language." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743710.

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Raj, Rohit, R. Sreemathy, Mousami Turuk, Jayashree Jagdale, and Mohammad Anish. "Indian Sign Language Recognition in Real Time using YOLO NAS." In 2024 3rd International Conference for Advancement in Technology (ICONAT). IEEE, 2024. https://doi.org/10.1109/iconat61936.2024.10774832.

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Arya, Chandrakala, Aarti Gusain, Kunal, Manoj Diwakar, Indrajeet Gupta, and Neeraj Kumar Pandey. "A Lightweight Solution for Real-Time Indian Sign Language Recognition." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947786.

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Sujatha, C., Prathamesh Jadi, N. B. Shubham, Sakshi Narayan Habib, U. M. Chaitanya, and Padmashree Desail. "Improved Indian Regional Sign Language Recognition with Extended IRKSL Dataset." In 2024 6th International Conference on Computational Intelligence and Networks (CINE). IEEE, 2024. https://doi.org/10.1109/cine63708.2024.10881001.

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Sharma, Vibhor, Anurag Singh, and Sabrina Gaito. "Indian Sign Language recognition and translation: An Encoder-Decoder Approach." In 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). IEEE, 2025. https://doi.org/10.1109/comsnets63942.2025.10885623.

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Mahendran, Shakana, and Ramya Mahendran. "Indian Sign Language Alphanumeric Hand Gestures Recognition Using Deep Learning Techniques." In 2025 5th International Conference on Advanced Research in Computing (ICARC). IEEE, 2025. https://doi.org/10.1109/icarc64760.2025.10963294.

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