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Artykuły w czasopismach na temat „Israeli Sign Language (ISL)”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Jaraisy, Marah, and Rose Stamp. "The Vulnerability of Emerging Sign Languages: (E)merging Sign Languages?" Languages 7, no. 1 (2022): 49. http://dx.doi.org/10.3390/languages7010049.

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Emerging sign languages offer linguists an opportunity to observe language emergence in real time, far beyond the capabilities of spoken language studies. Sign languages can emerge in different social circumstances—some in larger heterogeneous communities, while others in smaller and more homogeneous communities. Often, examples of the latter, such as Ban Khor Sign Language (in Thailand), Al Sayyid Bedouin Sign Language (in Israel), and Mardin Sign Language (in Turkey), arise in communities with a high incidence of hereditary deafness. Traditionally, these communities were in limited contact w

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Meir, Irit. "A Perfect Marker in Israeli Sign Language." Sign Language and Linguistics 2, no. 1 (1999): 43–62. http://dx.doi.org/10.1075/sll.2.1.04mei.

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In this paper I argue for the existence of an aspectual marker in Israeli Sign Language (ISL) denoting perfect constructions. This marker is the sign glossed as ALREADY. Though this sign often occurs in past time contexts, I argue that it is a perfect-aspect marker and not a past tense marker. This claim is supported by the following observations: (a) ALREADY can co-occur with past, present and future time adverbials; (b) its core meaning is to relate a resultant state to a prior event; (c) it occurs much more in dialogues than in narrative contexts. Further examination of the properties and f

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Meir, Irit. "Question and Negation in Israeli Sign Language." Sign Language and Linguistics 7, no. 2 (2006): 97–124. http://dx.doi.org/10.1075/sll.7.2.03mei.

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The paper presents the interrogative and negative constructions in Israeli Sign Language (ISL). Both manual and nonmanual components of these constructions are described, revealing a complex and rich system. In addition to the basic lexical terms, ISL uses various morphological devices to expand its basic question and negation vocabulary, such as compounding and suffixation. The nonmanual component consists of specific facial expressions, head and body posture, and mouthing. The use of mouthing is especially interesting, as ISL seems to use it extensively, both as a word formation device and a

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Lanesman, Sara, and Rose Stamp. "A Sociolinguistic Analysis of Name Signs in Israeli Sign Language." Sign Language Studies 25, no. 2 (2025): 293–324. https://doi.org/10.1353/sls.2025.a953724.

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Abstract: Name sign systems have been described in many deaf communities around the world. The most frequent name sign types are associated with an individual's appearance, for example, a signers' hairstyle, clothes, and physical features such as height, weight, etc. However, a recent study that examined name signs in Swedish Sign Language, for example, found a decrease in name signs based on appearance and an increase in person name signs, suggesting that name signs are undergoing changes. This study examines name signs produced by 160 deaf signers of Israeli Sign Language (ISL), a sign langu

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Novogrodsky, Rama, and Natalia Meir. "Age, frequency, and iconicity in early sign language acquisition: Evidence from the Israeli Sign Language MacArthur–Bates Communicative Developmental Inventory." Applied Psycholinguistics 41, no. 4 (2020): 817–45. http://dx.doi.org/10.1017/s0142716420000247.

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AbstractThe current study described the development of the MacArthur–Bates Communicative Developmental Inventory (CDI) for Israeli Sign Language (ISL) and investigated the effects of age, sign iconicity, and sign frequency on lexical acquisition of bimodal-bilingual toddlers acquiring ISL. Previous findings bring inconclusive evidence on the role of sign iconicity (the relationship between form and meaning) and sign frequency (how often a word/sign is used in the language) on the acquisition of signs. The ISL-CDI consisted of 563 video clips. Iconicity ratings from 41 sign-naïve Hebrew-speakin

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Fuks, Orit. "Intensifier actions in Israeli Sign Language (ISL) discourse." Gesture 15, no. 2 (2016): 192–223. http://dx.doi.org/10.1075/gest.15.2.03fuk.

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The study describes certain structural modifications employed on the citation forms of ISL during signing for intensification purposes. In Signed Languages, citation forms are considered relatively immune to modifications. Nine signers signed several scenarios describing some intense quality. The signers used conventional adverbs existing in ISL for intensification purposes. Yet, they also employed idiosyncratic modifications on the formational components of adjectives simultaneously to form realization. These optional modifications enriched the messages conveyed merely by the conventional for

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Sandler, Wendy, Gal Belsitzman, and Irit Meir. "Visual foreign accent in an emerging sign language." Special Issue in Memory of Irit Meir 23, no. 1-2 (2020): 233–57. http://dx.doi.org/10.1075/sll.00050.san.

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Abstract In the study of sign language phonology, little attention has been paid to the phonetic detail that distinguishes one sign language from another. We approach this issue by studying the foreign accent of signers of a young sign language – Al-Sayyid Bedouin Sign Language (ABSL) – which is in contact with another sign language in the region, Israeli Sign Language (ISL). By comparing ISL signs and sentences produced by ABSL signers with those of ISL signers, we uncover language particular features at a level of detail typically overlooked in sign language research. For example, within sig

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Stamp, Rose, Duaa Omar-Hajdawood, and Rama Novogrodsky. "Topical Influence: Reiterative Code-Switching in the Kufr Qassem Deaf Community." Sign Language Studies 24, no. 4 (2024): 771–802. http://dx.doi.org/10.1353/sls.2024.a936333.

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Abstract: Reiterative code-switching, when one lexical item from one language is produced immediately after a semantically equivalent lexical item in another language, is a frequent phenomenon in studies of language contact. Several spoken language studies suggest that reiteration functions as a form of accommodation, amplification (emphasis), reinforcement, or clarification; however, its function in sign language seems less clear. In this study, we investigate reiterative code-switching produced in semispontaneous conversations while manipulating two important factors: interlocutor and topic.

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Tkachman, Oksana, and Wendy Sandler. "The noun–verb distinction in two young sign languages." Where do nouns come from? 13, no. 3 (2013): 253–86. http://dx.doi.org/10.1075/gest.13.3.02tka.

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Many sign languages have semantically related noun-verb pairs, such as ‘hairbrush/brush-hair’, which are similar in form due to iconicity. Researchers studying this phenomenon in sign languages have found that the two are distinguished by subtle differences, for example, in type of movement. Here we investigate two young sign languages, Israeli Sign Language (ISL) and Al-Sayyid Bedouin Sign Language (ABSL), to determine whether they have developed a reliable distinction in the formation of noun-verb pairs, despite their youth, and, if so, how. These two young language communities differ from e

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Sandler, Wendy. "The Medium and the Message." Sign Language and Linguistics 2, no. 2 (1999): 187–215. http://dx.doi.org/10.1075/sll.2.2.04san.

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In natural communication, the medium through which language is transmitted plays an important and systematic role. Sentences are broken up rhythmically into chunks; certain elements receive special stress; and, in spoken language, intonational tunes are superimposed onto these chunks in particular ways — all resulting in an intricate system of prosody. Investigations of prosody in Israeli Sign Language demonstrate that sign languages have comparable prosodic systems to those of spoken languages, although the phonetic medium is completely different. Evidence for the prosodic word and for the ph

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Fuks, Orit. "The distribution of handshapes in the established lexicon of Israeli Sign Language (ISL)." Semiotica 2021, no. 242 (2021): 101–22. http://dx.doi.org/10.1515/sem-2019-0049.

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Abstract Our study focuses on the perception of the iconicity of handshapes – one of the formational parameters of the sign in signed language. Seventy Hebrew speakers were asked to match handshapes to Hebrew translations of 45 signs (that varied in degree of iconicity), which are specified for one of the handshapes in Israeli Sign Language (ISL). The results show that participants reliably match handshapes to corresponding sign translations for highly iconic signs, but are less accurate for less iconic signs. This demonstrates that there is a notable degree of iconicity in the lexicon of ISL,

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Ezra, Din, Shai Mastitz, and Irina Rabaev. "Signsability: Enhancing Communication through a Sign Language App." Software 3, no. 3 (2024): 368–79. http://dx.doi.org/10.3390/software3030019.

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The integration of sign language recognition systems into digital platforms has the potential to bridge communication gaps between the deaf community and the broader population. This paper introduces an advanced Israeli Sign Language (ISL) recognition system designed to interpret dynamic motion gestures, addressing a critical need for more sophisticated and fluid communication tools. Unlike conventional systems that focus solely on static signs, our approach incorporates both deep learning and Computer Vision techniques to analyze and translate dynamic gestures captured in real-time video. We

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Fuks, Orit. "Iconicity Perception under the Lens of Iconicity Rating and Transparency Tasks in Israeli Sign Language (ISL)." Sign Language Studies 24, no. 1 (2023): 46–92. http://dx.doi.org/10.1353/sls.2023.a912330.

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Abstract: This study undertook iconicity ratings and conducted transparency experiments on Israeli Sign Language (ISL). Experiment 1 compared the iconicity ratings of 520 lexical signs of ten Deaf ISL signers and thirteen hearing nonsigners. Ratings were found to be affected by language knowledge, lexical class, and type of iconic mapping, as well as by factors less connected to iconicity, such as a sense of familiarity with a form. In experiment 2, twenty nonsigners guessed the meaning of the 520 signs, and the correct guesses were correlated with the iconicity scores. Overall, nonsigners ten

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Dachkovsky, Svetlana. "From a demonstrative to a relative clause marker." Special Issue in Memory of Irit Meir 23, no. 1-2 (2020): 142–70. http://dx.doi.org/10.1075/sll.00047.dac.

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Abstract Demonstratives provide an important link between gesture, discourse and grammar due to their communicative function to coordinate the interlocutor’s focus of attention. This underlies their frequent cross-linguistic development into a wide range of function words and morphemes (Diessel 1999). The present study provides evidence for a link between gesture and grammar by tracking diachronic development of a relative clause marker in Israeli Sign Language (ISL) restrictive relative clauses, which starts as a gestural locative pointing sign, and grammaticalizes into a relative pronoun con

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Swead, Riki Taitelbaum, Yaniv Mama, and Michal Icht. "The Effect of Presentation Mode and Production Type on Word Memory for Hearing Impaired Signers." Journal of the American Academy of Audiology 29, no. 10 (2018): 875–84. http://dx.doi.org/10.3766/jaaa.17030.

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AbstractProduction effect (PE) is a memory phenomenon referring to better memory for produced (vocalized) than for non-produced (silently read) items. Reading aloud was found to improve verbal memory for normal-hearing individuals, as well as for cochlear implant users, studying visually and aurally presented material.The present study tested the effect of presentation mode (written or signed) and production type (vocalization or signing) on word memory in a group of hearing impaired young adults, sign-language users.A PE paradigm was used, in which participants learned lexical items by two pr

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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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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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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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Aadhya, Satrasala, and al. et. "Indian Sign Language Translator Using CNN." International Journal of Computational Learning & Intelligence 4, no. 4 (2025): 792–98. https://doi.org/10.5281/zenodo.15279424.

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This paper main focus is to create a real-time Indian Sign Language (ISL) translator designed to overcome the gap between the deaf and hard-of-hearing population and the hearing population. By leveraging computer vision techniques and machine learning models, the system can accurately recognize a wide range of ISL gestures and translate them into corresponding text outputs in English.  The application is intended to facilitate seamless communication, enhancing accessibility in various settings such as education, healthcare, and daily interactions. This solution aims to foster greater incl

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Wankhade, Vaishnavi. "Indian Sign Language Detection using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30798.

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Indian Sign Language (ISL) serves as a primary means of communication for millions of hearing-impaired individuals in India. However, the lack of comprehensive tools for interpreting ISL poses significant challenges in facilitating effective communication and integration of the deaf community into society. This research paper explores the advancements, challenges, and potential applications of Indian Sign Language detection technology. It provides an overview of existing techniques for ISL detection, including computer vision-based approaches and wearable devices. Additionally, the paper discu

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Das Chakladar, Debashis, Pradeep Kumar, Shubham Mandal, Partha Pratim Roy, Masakazu Iwamura, and Byung-Gyu Kim. "3D Avatar Approach for Continuous Sign Movement Using Speech/Text." Applied Sciences 11, no. 8 (2021): 3439. http://dx.doi.org/10.3390/app11083439.

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Sign language is a visual language for communication used by hearing-impaired people with the help of hand and finger movements. Indian Sign Language (ISL) is a well-developed and standard way of communication for hearing-impaired people living in India. However, other people who use spoken language always face difficulty while communicating with a hearing-impaired person due to lack of sign language knowledge. In this study, we have developed a 3D avatar-based sign language learning system that converts the input speech/text into corresponding sign movements for ISL. The system consists of th

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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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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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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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Sarmad Khan, Hafiz Muhammad, Simon D. Mcloughlin, and Irene Murtagh. "Comparative Evaluation and Utilization of Convolutional Neural Network Architectures for Irish Sign Language Recognition." International Journal of Combinatorial Optimization Problems and Informatics 16, no. 1 (2025): 123–31. https://doi.org/10.61467/2007.1558.2025.v16i1.550.

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Irish Sign Language (ISL) stands as a preferred mode of communication used by the deaf and hard-of-hearing community in Ireland. With its unique grammar, syntax, and lexicon, ISL plays a pivotal role in facilitating communication for thousands of individuals, reflecting centuries of cultural heritage and linguistic development. An estimated 5,000 Deaf individuals utilize ISL, with an additional 40,000 hearing individuals, spanning from regular to occasional users, also engaging with Irish Sign Language. Despite its cultural and linguistic importance, ISL faces numerous challenges in terms of t

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N, Sudiksha. "Talking Fingers: Bridging the Communication Gap through Real-Time Speech-to-Indian Sign Language Translation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40875.

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"Talking Fingers" is an innovative initiative to be developed to facilitate communication between hearing and non-hearing individuals by building a web-based system that can translate spoken language into Indian Sign Language (ISL). Being an essential means of communication among millions in India, ISL remains underdeveloped by technologies that are dominated by American and British Sign Languages. Current tools rely on the basic word-by-word translation with no contextual or grammatical accuracy. The proposed system will thus integrate speech recognition, NLP, and ISL visuals for real-time, c

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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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Patil, Gouri Shanker, R. Rangasayee, and Geetha Mukundan. "Non-fluent aphasia in deaf user of Indian Sign Language." Cognitive Linguistic Studies 1, no. 1 (2014): 147–53. http://dx.doi.org/10.1075/cogls.1.1.07pat.

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The current study describes aphasia in a deaf user of Indian Sign Language (ISL). One congenitally deaf adult with LHD was evaluated for signs of aphasia. The tools used were Aphasia Diagnostic Battery in Indian Sign Language (ADB in ISL), Magnetic Resonance Imaging (MRI) investigation, linguistic, and neurobehavioral profile. The results of all investigative procedures revealed signs and symptoms consistent with non-fluent aphasia specifically Broca’s aphasia. The data from ISL in brain damaged individual further emphasize the role of left hemisphere in sign language processing.

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Snehal, Pawar, Salunke Pragati, Mhasavade Arati, Bhutkar Aishwaraya, and R. Pathak K. "Real Time Identification of American Sign Language for Deaf and Dumb Community." Advancement in Image Processing and Pattern Recognition 2, no. 3 (2020): 1–7. https://doi.org/10.5281/zenodo.3600015.

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<em>The only way for deaf and dumb for communication is based on sign language which involves hand gestures. In this system, we are working on the American Sign Language (ASL) dataset (A-Z), (0-9) and word alphabet identification escort by our word identification dataset of Indian Sign Language (ISL). Sign data samples to be making our system more faultless, error free, and unambiguous with help of Convolutional Neural Network (CNN). Today, much research has been going on the field of sign language recognition but existing study failed to develop trust full communication interpreter. The motiv

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R, Thirumahal, Aswath Harish Jayaprakash, Shiva Prakash P, Yuvaraj Kesavan P, Chirenjeevi M, and Siva M. "Machine Learning based ISL Identification and Translation." Journal of Ubiquitous Computing and Communication Technologies 6, no. 4 (2024): 353–67. https://doi.org/10.36548/jucct.2024.4.003.

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Sign language is an essential means of communication for the deaf and hard-of-hearing community. However, effective communication between sign language users and those unfamiliar with sign language can be challenging. The primary goal is to utilize the machine learning to automatically identify sign language gestures and translate them into easily understandable formats. This research presents a comprehensive sign language detection system that captures sign language gestures, detects them, and provides output in text using LSTM (Long Short-Term Memory) and Transformers with an accuracy of 79%

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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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Mistree, Kinjal, Devendra Thakor, and Brijesh Bhatt. "A Machine Translation System from Indian Sign Language to English Text." International Journal of Information Technologies and Systems Approach 15, no. 1 (2022): 1–23. http://dx.doi.org/10.4018/ijitsa.313419.

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Sign language recognition and translation is a crucial step towards improving communication between the deaf and the rest of the society. According to the Indian Sign Language Research and Training Centre (ISLRTC), India has around 300 certified human interpreters. With such a shortage of human interpreters, an alternative service is desired that helps people to achieve smooth communicate with deaf. In this study, an approach is presented that translates ISL sentences in English text using MobileNetV2 model and neural machine translation (NMT). The system features ISL corpus created from Brown

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Ajay M. Pol, Et al. "Enhancing Sign Language Recognition through Fusion of CNN Models." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 902–10. http://dx.doi.org/10.17762/ijritcc.v11i10.8608.

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This study introduces a pioneering hybrid model designed for the recognition of sign language, with a specific focus on American Sign Language (ASL) and Indian Sign Language (ISL). Departing from traditional machine learning methods, the model ingeniously blends hand-crafted techniques with deep learning approaches to surmount inherent limitations. Notably, the hybrid model achieves an exceptional accuracy rate of 96% for ASL and 97% for ISL, surpassing the typical 90-93% accuracy rates of previous models. This breakthrough underscores the efficacy of combining predefined features and rules wi

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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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Attar, Rakesh Kumar, Vishal Goyal, and Lalit Goyal. "Development of Airport Terminology based Synthetic Animated Indian Sign Language Dictionary." Journal of Scientific Research 66, no. 05 (2022): 88–94. http://dx.doi.org/10.37398/jsr.2022.660512.

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In the current era of computerization, the development of a synthetic animated Indian Sign Language (ISL) dictionary could prove very beneficial for deaf people to share their ideas, views and thoughts with hearing people. Although many human based video dictionaries are available, no ISL synthetic animated dictionary solely for public places is developed yet. The development of an ISL dictionary of 1200 words using synthetic animation for airports terminology is reported in this article. The most frequently used words at airports in ISL are categorized and then are translated into Signing Ges

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Anjali, Mogusala. "Contextual Translation System to Sign Language." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41320.

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People with hearing and speech disabilities face significant challenges in communicating with others, as not everyone understands sign language. This project aims to create a system that helps bridge this communication gap by converting spoken English into Indian Sign Language (ISL). The system works by recognizing voice input, the recognized speech is converted into text, which is then simplified using natural language processing techniques. Finally, the text is translated into ISL and displayed as a series of images or motion videos using Python libraries. This system provides an easy and ac

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G,, Anvith. "Talking Fingers: A Multilingual Speech-to-Sign Language Converter." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40782.

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Communication is a basic human need, but thousands of people with hearing and speech impairments face limitations in everyday communication "Talking Fingers" is a modern assistive technology tool that transfers spoken or written language to Indian Sign Language (ISL). With multilingual capabilities, the tool provides technologies such as Google ML Kit for language recognition, MyMemory API for translation, ISL grammar services for more than one language and several languages accessible at a time Translated ISL will, a it provides a simple and powerful communication channel. The system outlines

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El Zaar, Abdellah, Nabil Benaya, and Abderrahim El Allati. "Sign Language Recognition: High Performance Deep Learning Approach Applyied To Multiple Sign Languages." E3S Web of Conferences 351 (2022): 01065. http://dx.doi.org/10.1051/e3sconf/202235101065.

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In this paper we present a high performance Deep Learning architecture based on Convolutional Neural Network (CNN). The proposed architecture is effective as it is capable of recognizing and analyzing with high accuracy different Sign language datasets. The sign language recognition is one of the most important tasks that will change the lives of deaf people by facilitating their daily life and their integration into society. Our approach was trained and tested on an American Sign Language (ASL) dataset, Irish Sign Alphabets (ISL) dataset and Arabic Sign Language Alphabet (ArASL) dataset and o

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Yerpude, Poonam. "Non-Verbal (Sign Language) To Verbal Language Translator Using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 269–73. http://dx.doi.org/10.22214/ijraset.2022.39820.

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Abstract: Communication is very imperative for daily life. Normal people use verbal language for communication while people with disabilities use sign language for communication. Sign language is a way of communicating by using the hand gestures and parts of the body instead of speaking and listening. As not all people are familiar with sign language, there lies a language barrier. There has been much research in this field to remove this barrier. There are mainly 2 ways in which we can convert the sign language into speech or text to close the gap, i.e. , Sensor based technique,and Image proc

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Shahi, Mr Shivanshu. "Multilevel Conversion of Indian Sign Language from Gesture to Speech." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5764–70. https://doi.org/10.22214/ijraset.2025.71548.

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Abstract: Indian Sign Language (ISL) serves as a primary mode of communication for Deaf and hard-of-hearing communities in India. However, despite its societal importance, ISL remains largely unsupported by mainstream technological platforms, limiting inclusive communication. This research introduces a real-time ISL recognition andtranslationsystem thatconvert shandgesturesintocorresponding text and speech outputs, enabling phrase-level communication rather thanisolated characterinterpretation. The architecture uses a modular pipeline approach, with a Convolutional Neural Network (CNN) for acc

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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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Zeshan, Ulrike, and Sibaji Panda. "Sign-speaking: The structure of simultaneous bimodal utterances." Applied Linguistics Review 9, no. 1 (2018): 1–34. http://dx.doi.org/10.1515/applirev-2016-1031.

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AbstractWe present data from a bimodal trilingual situation involving Indian Sign Language (ISL), Hindi and English. Signers are co-using these languages while in group conversations with deaf people and hearing non-signers. The data show that in this context, English is an embedded language that does not impact on the grammar of the utterances, while both ISL and Hindi structures are realised throughout. The data show mismatches between the simultaneously expressed ISL and Hindi, such that semantic content and/or syntactic structures are different in both languages, yet are produced at the sa

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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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Nupur Giri. "Gesturely: A Conversation AI based Indian Sign Language Model." Journal of Information Systems Engineering and Management 10, no. 10s (2025): 576–84. https://doi.org/10.52783/jisem.v10i10s.1421.

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The project, Gesturely, aims to improve communication in educational settings for people with hearing impairments. Sign language, notably Indian Sign Language (ISL) in India, serves as a primary mode of expression for the deaf community. The form of expression among the deaf relies on a rich vocabulary of gestures involving fingers, hands, arms, eyes, head, and face. The research endeavors to develop an algorithm capable of translating ISL into English, initially focusing on words within the education domain. Through the integration of advanced computer vision and deep learning methodologies,

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Gudi, Swaroop. "Sign Language Detection Using Gloves." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 1387–91. http://dx.doi.org/10.22214/ijraset.2024.65315.

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This paper presents a comprehensive system for real-time translation of Indian Sign Language (ISL) gestures into spoken language using gloves equipped with flex sensors. The system incorporates an Arduino Nano microcontroller for data acquisition, an HC-05 Bluetooth module for wireless data transmission, and an Android application for processing. A deep learning model, trained on an ISL dataset using Keras and TensorFlow, classifies the gestures. The processed data is then converted into spoken language using Google Text-to-Speech (GTTS). The gloves measure finger movements through flex sensor

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Chakole, Vijay V. "Educational Learning-Based Sign Language System Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29753.

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This study proposes an innovative approach to multicultural education by integrating Indian Sign Language (ISL) and American Sign Language (ASL) through Machine Learning (ML) techniques. By collecting and preprocessing high-quality video data of ISL and ASL, we aim to develop ML models capable of recognizing and generating signs in both languages. Through bidirectional transfer learning and cross-language representation learning, we seek to enhance the learning experience and address common challenges in sign language acquisition. Additionally, personalized learning environments and culturally

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Nespor, Marina, and Wendy Sandler. "Prosody in Israeli Sign Language." Language and Speech 42, no. 2-3 (1999): 143–76. http://dx.doi.org/10.1177/00238309990420020201.

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S. Nikkam, Pushpalatha. "Voice To Sign Language Conversion." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48637.

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ABSTRACT True incapacity can be seen as the inability to speak, where individuals with speech impairments struggle to communicate verbally or through hearing. To bridge this gap, many rely on sign language, a visual method of communication that uses hand gestures. Although sign language has become more widespread, interaction between those who sign and those who don't can still pose challenges. As communication has grown to be an essential part of daily life, sign language serves as a crucial tool for those with speech and hearing difficulties. Recent advances in computer vision and deep learn

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P, Adithyaraaj R., Mariyammal N, Mohammed Furkhan S, Rathika, and Prof K. Vijayalakshmi. "Indian Sign Language (ISL) Translator: AI-Powered Bidirectional Translation System." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2053–61. https://doi.org/10.22214/ijraset.2025.67589.

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Abstract: This work presents an advanced AI-based translation system designed to bridge communication barriers for the Deaf and Hard-of-Hearing (DHH) community by converting spoken and textual language into Indian Sign Language (ISL) and vice versa. The system leverages deep learning techniques, including computer vision and natural language processing (NLP), to interpret hand gestures and facial expressions accurately. Integrated with real-time processing capabilities, the model enables seamless interaction between ISL users and non-signing individuals. By utilizing a custom-trained Transform

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Mangai .V. "Comparative Analysis of Various Yolo Models for Sign Language Recognition with a specific dataset." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 544–50. https://doi.org/10.52783/jisem.v10i43s.8443.

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Understanding and replaying sign language are the hardcore communication tasks between the normal person and to deaf and dumped person, and vice versa. To enhance sign language-based communication, several models have been developed for making sign language into an understandable format by translating gestures into words. The ultimate goal of this research paper is to analyse and compare the various You Only Look One (YOLO) models on SLR problem. YOLO is a fast and efficient convolutional neural networks (CNN) variant that provides a better solution for sign language problems. The comparison o

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