Relevant bibliographies by topics / Sign language recognition

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  2. Dissertations / Theses
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  4. Book chapters
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Academic literature on the topic 'Sign language recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "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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Esenalieva, Gulzada, Mohd Tauheed Khan, Andrei Ermakov, and Eliza Tursunbekovna. "REAL-TIME SIGN LANGUAGE RECOGNITION." Alatoo Academic Studies 24, no. 1 (2024): 165–74. http://dx.doi.org/10.17015/aas.2024.241.15.

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Individuals who are deaf or mute frequently encounter communication barriers. Nevertheless, recent progress in artificial intelligence (AI) has mitigated these challenges. A method employing an open-source tool known as MediaPipe, along with OpenCV and a machine learning algorithm, facilitates the recognition of sign language. This system achieves real-time sign language recognition without relying on specialized wearable devices, enhancing convenience and accessibility. In this context, machine learning plays a pivotal role in recognizing and interpreting sign language, thus facilitating comm
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P.R., Mahidar. "Sign Language Recognition Techniques - A Survey." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 2747–60. http://dx.doi.org/10.37200/ijpr/v24i5/pr201978.

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Juluru, Saiteja, Bhavya Empati, Deepak Kallepalli, and Anitha V. "Sign Language Recognition using Open CV." International Journal of Science and Research (IJSR) 11, no. 5 (2022): 606–10. http://dx.doi.org/10.21275/sr22503220851.

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P, Keerthana, Nishanth M, Karpaga Vinayagam D, Alfred Daniel J, and Sangeetha K. "Sign Language Recognition." International Research Journal on Advanced Science Hub 3, Special Issue ICARD 3S (2021): 41–44. http://dx.doi.org/10.47392/irjash.2021.060.

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Jadhav, Akshay, Gayatri Tatkar, Gauri Hanwate, and Rutwik Patwardhan. "Sign Language Recognition." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 3 (2017): 109–15. http://dx.doi.org/10.23956/ijarcsse/v7i3/0127.

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Dubey, Shriya, Smrithi Suryawanshi, Aditya Rachamalla, and K. Madhu Babu. "Sign Language Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 386–92. http://dx.doi.org/10.22214/ijraset.2023.48586.

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Abstract: People communicate using sign language by visually conveying sign patterns to portray purpose. One method of communicating with deaf-mute people is to use sign language mechanisms. One of the nonverbal communication strategies used in sign language is the hand gesture. Many manufacturers all over the world have created various sign language systems, but they are neither adaptable nor cost-effective for end users. We present a design that can recognize various American sign language static hand motions in real-time using transfer learning, Python, and OpenCV in this paper. “Hello, Yes
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J, Yuvasri, Sujitha S, Pavithra B, Kamali K, and Rajalakshmi S. "Sign Language Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2643–47. http://dx.doi.org/10.22214/ijraset.2023.52128.

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Abstract: All over the world, the hearing and speaking impaired person communicate through the sign language as it is the only reliable source of interaction with themselves and as well as with normal people. This language is mainly made up ofthe shape of the hand movement and gestures it made. This system aims to bridge this communication gap and aid the deaf and the mute to use technology to carryout their daily transactions by using a simple approach which is easily implementable. Also, to help the hearing and speaking impaired person to communicate with people who do not understand sign la
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Pal, Divyanshu, Asst Professor Rohini Sharma, Dheeraj, Abdul Bazid, and Gaurav Chandel. "Sign Language Recognition." International Journal for Research in Applied Science and Engineering Technology, no. 6 (June 30, 2024): 197–201. http://dx.doi.org/10.22214/ijraset.2024.63056.

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Abstract: Communication through signs has consistently been a significant way for communication among hearing and speech impaired humans, generally called deaf and dumb. It is the only mode of communicating for such individuals to pass on their messages to other human beings, and hence other humans need to comprehend their language. In this project, a sign language detection or recognition web framework is proposed with the help of image processing. This application would help in recognizing Sign Language. The dataset used is the Sign Language dataset. This application could be used in schools
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Bhalerao, Prof R. S. "Sign Language Recognition." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6349–55. https://doi.org/10.22214/ijraset.2025.71693.

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This paper presents a novel real-time sign language detection system designed to enhance communication between the deaf and hard-of-hearing community and non-signers. Utilizing standard web cameras, the system captures and analyses hand and facial gestures, employing advanced computer vision and deep learning techniques to recognize sign language gestures. Key markers corresponding to specific signs are identified and translated into voice output and on-screen text, providing a dualoutput feature that fosters inclusivity and accessibility. By enabling real-time interpretation through voice and
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Dissertations / Theses on the topic "Sign language recognition"

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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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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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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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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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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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Brashear, Helene Margaret. "Improving the efficacy of automated sign language practice tools." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34703.

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The CopyCat project is an interdisciplinary effort to create a set of computer-aided language learning tools for deaf children. The CopyCat games allow children to interact with characters using American Sign Language (ASL). Through Wizard of Oz pilot studies we have developed a set of games, shown their efficacy in improving young deaf children's language and memory skills, and collected a large corpus of signing examples. Our previous implementation of the automatic CopyCat games uses automatic sign language recognition and verification in the infrastructure of a memory repetition and phr
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Books on the topic "Sign language recognition"

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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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Grobel, Kirsti. Videobasierte Gebärdenspracherkennung mit Hidden-Markov-Modellen. VDI Verlag, 1999.

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E, Johnson Robert, ed. RSVP: Fingerspelled word recognition through rapid serial visual presentation. DawnSignPress, 2011.

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De Meulder, Maartje, Joseph J. Murray, and Rachel L. McKee, eds. TheLegal Recognition of Sign Languages. Multilingual Matters, 2019. http://dx.doi.org/10.21832/9781788924016.

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Eleni, Efthimiou, Kouroupetroglou Georgios, and Fotinia Stavroula-Evita, eds. Gesture and sign language in human-computer interaction and embodied communication: 9th International Gesture Workshop, GW 2011, Athens, Greece, May 25-27, 2011 : revised selected papers. Springer, 2012.

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Shweta, Dour. Real Time Recognition of Indian Sign Language. Blurb, Incorporated, 2022.

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Murray, Joseph J., Maartje De Meulder, and Rachel L. McKee. Legal Recognition of Sign Languages: Advocacy and Outcomes Around the World. Multilingual Matters, 2019.

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Murray, Joseph J., Maartje De Meulder, and Rachel L. McKee. Legal Recognition of Sign Languages: Advocacy and Outcomes Around the World. Multilingual Matters, 2019.

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Murray, Joseph J., Maartje De Meulder, and Rachel L. McKee. Legal Recognition of Sign Languages: Advocacy and Outcomes Around the World. Multilingual Matters, 2019.

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Murray, Joseph J., Maartje De Meulder, and Rachel L. McKee. Legal Recognition of Sign Languages: Advocacy and Outcomes Around the World. Multilingual Matters, 2019.

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Book chapters on the topic "Sign language recognition"

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Cooper, Helen, Brian Holt, and Richard Bowden. "Sign Language Recognition." In Visual Analysis of Humans. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-997-0_27.

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Othman, Achraf. "Sign Language Recognition." In Sign Language Processing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68763-1_8.

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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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Holden, Eun-Jung, and Robyn Owens. "Visual Sign Language Recognition." In Multi-Image Analysis. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45134-x_20.

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Singh, Elina, Aditi Balaji, and K. Arthi. "Sign language recognition model." In Challenges in Information, Communication and Computing Technology. CRC Press, 2024. http://dx.doi.org/10.1201/9781003559085-146.

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Cooper, Helen, Eng-Jon Ong, Nicolas Pugeault, and Richard Bowden. "Sign Language Recognition Using Sub-units." In Gesture Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57021-1_3.

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Lang, Simon, Marco Block, and Raúl Rojas. "Sign Language Recognition Using Kinect." In Artificial Intelligence and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29347-4_46.

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Sapkota, Kanchan, Santosh Khandal, Sailesh Rana, Yugaraj Tamang, and Pankaj Sharma. "Dynamic Nepali Sign Language Recognition." In Advanced Computer Science Applications. Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003369066-17.

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Sagar, Laxmi Kant, Kartik Kumar, Akshit Goyal, Riya Singh, and Anubhaw Kumar Soni. "Sign Language Recognition Using AI." In Sustainable Computing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-13577-4_8.

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Edwards, Alistair D. N. "Progress in sign language recognition." In Gesture and Sign Language in Human-Computer Interaction. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0052985.

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Conference papers on the topic "Sign language recognition"

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Gupta, Rohan, Chirag Pandit, Krishnam Gupta, and Prabhjot Singh. "Real-Time Sign Language Recognition." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739073.

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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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Karche, Avishkar S., Avadhoot V. Kamble, Krupa A. Maru, Sumedh S. Kedari, and Dikshendra D. Sarpate. "American Sign Language Recognition Application." In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2025. https://doi.org/10.1109/esci63694.2025.10988218.

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Zou, Jingchen, Jianqiang Li, Jing Tang, Yuning Huang, Shujie Ding, and Xi Xu. "Sign Language Recognition and Translation Methods Promote Sign Language Education: A Review." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831194.

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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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Saini, Shivani, Meenu Prjapati, Bhupal Arya, Ravinder Kumar Sharma, and Manoj Kumar. "Understanding Sign Language Recognition: An Overview." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739257.

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Shinde, Swapnil, Parikshit Mahalle, Sayee Panchal, Shreya Mahalle, Atharva Pandit, and Parag Tonpe. "Sign language recognition using deep learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725481.

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Rangu, Neha, Preetham Dathi, Pavan Kethavath, M. Suneetha, and K. Jamal. "Sign Language Recognition using Transfer Learning." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10627077.

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Pahlevanzadeh, Maryam, Mansour Vafadoost, and Majid Shahnazi. "Sign language recognition." In 2007 9th International Symposium on Signal Processing and Its Applications (ISSPA). IEEE, 2007. http://dx.doi.org/10.1109/isspa.2007.4555448.

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Schioppo, Jacob, Zachary Meyer, Diego Fabiano, and Shaun Canavan. "Sign Language Recognition." In CHI '19: CHI Conference on Human Factors in Computing Systems. ACM, 2019. http://dx.doi.org/10.1145/3290607.3313025.

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