Gesture Recognition Glove For American Sign Language Using Accelerometers

The Shape of Air: American Sign Language as Narrative Prosthesis in 21st Century North American Media

The project "Transforming Expression into Language: American Sign Language Recognition using Image Processing" intends to employ image processing techniques to identify gestures in American Sign Language (ASL). The goal of this project is to develop an efficient system for translating ASL signs using visual cues generated from hand motions and gestures. The program intends to use image processing technology to bridge the communication gap between ASL users and nonusers. The use of image processing for ASL identification may improve communication in a range of situations while also increasing accessibility for the deaf community. The paper provides a realistic approach to real-time ASL motion interpretation using image processing techniques, thereby advancing the field of sign language recognition technology. KEYWORDS ASL Recognition, Image Processing Techniques, Convolutional Neural Networks (CNNs), Feature Extraction in Image Processing, Transforming Expressions into Text

Sign language plays a crucial role as a communication tool for both the deaf and hard-of- hearing communities, enabling them to engage and interact effectively within their own community as well as with others.However, communication barriers arise when individuals unfamiliar with sign language engage with those who rely on it, underscoring the need for inclusive solutions. Real-time sign language interpretation systems, leveraging machine learning and computer vision technologies, present a promising approach to bridging this gap. These systems convert sign language gestures into spoken or written language by utilizing gesture recognition algorithms, neural networks, and natural language processing. By analyzing hand movements, facial expressions, and body language, the systems provide accurate, context-aware translations of various sign languages, such as American Sign Language (ASL), with minimal delay. This enables seamless, natural interactions, making such technologies essential for fostering inclusive communication in diverse settings. Key Words: Sign language recognition, real- time interpretation, machine learning, com- puter vision, gesture recognition, neural networks, natural language processing, communication barriers, inclusivity, American Sign Language (ASL).

Contents: Preface. Part I: Reminiscences. L. Fant, Two Memorable Meals With Ursula and Ed. R.M. Battison, American Sign Language Linguistics 1970-1980: Memoir of a Renaissance. B. Bragg, A Fish Story for Ursula and Other Remembrances. Part II: Historical and Comparative Analyses of Sign Languages. J. Woodward, Sign Languages and Sign Language Families in Thailand and Vietnam. D. McKee, G. Kennedy, Lexical Comparisons of Signs From American, Australian, British, and New Zealand Sign Languages. H. Lane, R.C. Pillard, M. French, Origins of the American Deaf-World: Assimilating and Differentiating Societies and Their Relation to Genetic Patterning. Part III: Language in the Visual-Spatial Modality. E.L. Newport, T. Supalla, Sign Language Research at the Millennium. P. Siple, Attentional Resources and Working Memory: A New Framework for the Study of the Impact of Deafness on Cognition. M. Wilson, K. Emmorey, When Does Modality Matter? Evidence From ASL on the Nature of Working Memory. P.B. Braem, T. Bram, A Pilot Study of the Expressive Gestures Used by Classical Orchestra Conductors. N. Frishberg, An Interpreter Creates the Space. Part IV: Linguistic Analysis of Sign Languages. S.D. Fischer, More Than Just Handwaving: The Mutual Contributions of Sign Language and Linguistics. R.B. Wilbur, Phonological and Prosodic Layering of Nonmanuals in American Sign Language. R.C. Loew, C.T. Akamatsu, M. Lanaville, A Two-Handed Manual Alphabet in the United States. E. Pizzuto, V. Volterra, Iconicity and Transparency in Sign Languages: A Cross-Linguistic Cross-Cultural View. M. Kuntze, Codeswitching in ASL and Written English Language Contact. S.K. Liddell, Indicating Verbs and Pronouns: Pointing Away From Agreement. Part V: Language Acquisition. J.G. Bettger, Viewing Deaf Children in a New Way: Implications of Bellugi and Klima's Research for Education. R.P. Meier, Shared Motoric Factors in the Acquisition of Sign and Speech. A. Abrahamsen, Explorations of Enhanced Gestural Input to Children in the Bimodal Period. D. Lillo-Martin, Early and Late in Language Acquisition: Aspects of the Syntax and Acquisition of Wh-Questions in American Sign Language. J. Reilly, Bringing Affective Expression Into the Service of Language: Acquiring Perspective Marking in Narratives. C.A. Padden, V.L. Hanson, Search for the Missing Link: The Development of Skilled Reading in Deaf Children. L.A. Petitto, On the Biological Foundations of Human Language. Part VI: The Neural Organization of Sign Language. A.R. Damasio, H. Damasio, Language and the Brain. D.P. Corina, Some Observations Regarding Paraphasia in American Sign Language. H. Poizner, D. Brentari, M.E. Tyrone, J. Kegl, The Structure of Language as Motor Behavior: Clues From Signers With Parkinson's Disease. V.A. Fromkin, On the Uniqueness of Language.

Sign language identification and recognition technique is composed by the gesture images detection and the hand gestures recognition. Gesture images detection is to locate the image part of palm and fingers from the captured pictures, and rotating them to the appropriate gesture posture. Both of them are the important pre-processing for sign language identification and recognition. Lose them, the correctness rate of the sign language recognition algorithms will be dropped down to an unacceptable level. This paper presents novel processing algorithms for the gesture images detection and recognition. In the detection process, it rotates an askew gesture to right position, and to delete the elbow and forearm parts from the captured pictures. In the recognition process, it includes two phases with the model construction and the sign language identification. In the model construction phase, the static hand gesture of sign language is constructed by the Gaussian mixture model, and the unknown gesture image is identified by Gaussian model match. Based on this presented static sign language detection and recognition algorithms, the correct recognition rate is about 94% in average.

You have accessThe ASHA LeaderFeature1 Apr 2003Serving Clients Who Use Sign Language Susanne Scott, and James H. Lee Susanne Scott Google Scholar More articles by this author and James H. Lee Google Scholar More articles by this author https://doi.org/10.1044/leader.FTR1.08062003.6 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In Many speech-language and hearing professionals now encounter clients who use sign languages as their primary mode of communication. Can these professionals serve these clients, or do they need to get interpreters or make referrals? ASHA is committed to meeting the needs of culturally and linguistically diverse populations. All too often “linguistically diverse” is assumed to refer to users of spoken languages other than English. However, individuals who use a signed language, whether American Sign Language (ASL) or some other sign system, are also linguistically diverse. It is difficult, if not impossible, to determine how many people in the United States use some form of sign language to communicate. It might be easier to try to determine the number of signing individuals that are seen in hearing and speech centers and in educational placements throughout the United States. In keeping with ASHA’s vision, it is important to ensure that there are an adequate number of speech-language and hearing professionals to serve this population. Although it may be necessary to use interpreters in some situations or locales or to make referrals to larger urban areas, programs, clinics, and schools throughout the country should be striving to better and more directly serve clients who use sign. The Population Clients who use a sign language/system are a very diverse population. Not only do these individuals differ in age, gender, race, ethnicity, national origin, religion, sexual orientation, and socio-economic status, but also in the sign language/system that they use. Often the sign language/system used is determined by the individual’s identity or, in the case of a child, the educational placement. An individual who identifies as a member of the deaf community/culture in the United States will most likely use American Sign Language. Individuals who depend on their residual hearing and identify as hard of hearing or hearing impaired, will most likely use an invented sign system (i.e., SEE II, Signed English) or more English-based sign. Children who are deaf or hard of hearing may use ASL, a sign system, or English-based sign as determined by the school’s educational/communication philosophy (see sidebar for definitions). Cultural and Linguistic Competence Cultural and linguistic competence can enhance clinical practice by: increasing the bond/relationship between the clinician and the client and the client’s significant others demonstrating respect and sensitivity enhancing service provision facilitating achievement of goals and carryover/generalization of skills And, importantly, it’s the right thing to do! The National Center for Cultural Competence ( www.georgetown.edu/ research/gucdc/ nccc/faqs.html) has identified six overarching reasons why cultural competence is important to health care providers. In brief, they are: 1. To respond to current and projected demographic changes in the United States 2. To eliminate long-standing disparities in the health status of people of diverse racial, ethnic, and cultural backgrounds 3. To improve the quality of services and health outcomes 4. To meet legislative, regulatory, and accreditation mandates 5. To gain a competitive edge in the market place 6. To decrease the likelihood of liability/malpractice claims Strategies Clinicians who are able to sign should apply the following strategies in order to meet the communication needs of their clients: Respect the communication mode of your clients. The clinician should try to match the client’s mode of communication. Be willing to admit when you do not understand. Try not to “bluff” or use a passive behavior. Speech-language-hearing professionals should model appropriate communication strategies such as confirmation and repair strategies when the clinician is not able to understand the client’s signs. Use facial expressions and body movements with signs. Facial expressions and body movements are fundamental features of ASL. In spoken English, speakers often signal a question by using a rising vocal intonation. In ASL, users do so by raising the eyebrows and widening the eyes. ASL users may ask a question by tilting their bodies forward while signaling with their eyes and eyebrows. Try to be conceptually correct in the use of signs. Choose signs that appropriately convey the information you are trying to communicate. Remember each sign in a signed language/system is composed of a specific, unique combination of a hand shape, movement, palm orientation, and location. Changing any one of these aspects of the sign changes the meaning of the sign. Always fingerspell technical terms (e.g., audiogram, frequency) first before assigning a “made up” sign (e.g., “A” hand shape circling in front of the ear + sign for graph = audiogram; “F” hand shape moving like a “wave” from left to right = frequency). Always use visual aids to supplement your signs. Use pictures of the ear and the “audiogram of familiar sounds” to help explain unfamiliar concepts (e.g., type of hearing loss; sounds the client may never have heard). Working With an Interpreter If necessary, it may be best to obtain the services of a certified interpreter for the deaf. Certified interpreters follow a strict Code of Ethics developed by the Registry of Interpreters of the Deaf (RID). Interpreters can hold different types of RID certification. For example, a CI (Certificate of Interpretation) certificate indicates the interpreter can sign from English to ASL and ASL to English. A CT (Certificate of Transliteration) certificate means the interpreter has been evaluated and is proficient in interpreting from spoken English to a more English-based sign and vice versa. Some RID interpreters hold both certificates. In addition, the National Association of the Deaf also certifies interpreters using a system with varying skill levels. Finally, some states also have certification programs and tests for interpreters. It is essential to find out what is required in your area and obtain the services of qualified interpreters. When working with an interpreter, speech-language or hearing professionals should remember that they are consumers of interpreting services. Be sure to establish what you and your signing client need from the interpreter. For example, some signing individuals have intelligible speech and the interpreter would not need to voice what the signer expresses. Other times the speech may not be intelligible enough and the interpreter may need to voice the intent of the signing client. Finally, it is important to remember that the interpreter is a conduit of information between the clinician and the client. The signing client is addressed directly. The client should not be addressed through the interpreter (i.e., “Tell her…”). Instead the professional should speak directly to the client and allow the interpreter to convey the information. To facilitate this, it is often helpful to position the interpreter next to the hearing or speech-language professional and across from the signing client. This allows for a good visual environment. The signing client can see the interpreter and the professional. During interactions as much eye contact as possible should be maintained with the client. The signing client will need to divide the visual attention between the professional and the interpreter but the professional should remain focused on the client. Definitions American Sign Language (ASL) ASL is a complete, complex language that incorporates the use of hand shapes, position, palm orientation and movement, facial expressions, body movements and postures, and gestures and other visual cues to form its words. It is completely separate from English. ASL has its own rules for grammar, morphology, and sentence order. It is the first language of many North Americans who are deaf and is the fourth most commonly used language in the United States (www.aslinfo.com). Deaf Community/Culture A multi-cultural group of individuals who identify themselves as Deaf, as an ethnic identity, and not a physical condition. The Deaf community is composed of people who use American Sign Language as their primary means of communication. The Deaf Community considers itself a linguistic minority group, a separate entity because of its unique culture, language, and social norms. It is separate from other disability consumer groups by the virtue of communication process, not physical disabilities (www.signmedia.com/info/adc.htm). Finger Spelling (Dactylology) and Invented Sign Systems Finger spelling uses hand shapes to represent the letters of the English alphabet. Invented Sign Systems (Signing Exact English [SEE] and Signed English [SE]) use manual codes (use of the hands) to represent the structure of English. English-Based Signing English-based signing entails signing a combination of both English and ASL features executed in English word order. The language proficiency of the signer will determine the use of the features from either language. It is different from the invented sign systems because it does not follow a clear-cut set of principles. In the past, this type of signing has been labeled pidgin signed English (PSE) and more recently referred to as sign English or conceptually accurate sign English (CASE). It was not intended to model English or be used in an educational context or to teach English. Its purpose is for social communication. Print Publications Resources Paul P. V. (2001). Language and Deafness (3rd ed.). San Diego, CA: Thomson Learning. Google Scholar Vold F. C., Kinsella-Meier M., & Hughes Hilley M. C. (1990). Signing with your client: A practical manual for audiologists and speech/language pathologists. Washington DC: Gallaudet University Press. Google Scholar Web www.nad.org/infocenter/infotogo/dcc/difference.html http://clerccenter.gallaudet.edu/InfoToGo/549.html www.oregon.gov/DHS/odhhs/ References Author Notes Susanne Scott, is a supervising aural rehabilitationist in the department of audiology and speech-language pathology at Gallaudet University. In addition to clinical supervision, she teaches the Clinical Applications of Sign Communication classes to students in the clinical doctorate of audiology program. Contact her by e-mail at [email protected]. James H. Lee, is a clinical supervisor in the department of audiology and speech-language pathology at Gallaudet University. In addition to clinical supervision, he teaches Clinical Applications of Sign Communication to speech-language pathology graduate students. Contact him by e-mail at [email protected]. Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetails Volume 8Issue 6April 2003 Get Permissions Add to your Mendeley library History Published in print: Apr 1, 2003 Metrics Current downloads: 4,634 Topicsasha-topicsleader_do_tagasha-article-typesCopyright & Permissions© 2003 American Speech-Language-Hearing AssociationLoading ...

Introduction In this paper, I examine the practices of representing Deaf ‘voices’’ to hearing audiences in two recent US television crime dramas. More literally I look at how American Sign Language is framed and made visible on the screen through various production decisions. Drawing examples from an episode of CSI: New York that aired in December 2006 and an episode of Law and Order: Criminal Intent that aired in April 2007, I examine how the practices of filming Deaf people and the use of American Sign Language intersect with the production of a Deaf ‘voice’ on the screen. The problem of representing a Deaf ‘voice’ on the screen is akin to the problem of representing other minority languages. Film and television producers in the United States have to make choices about whether the majority audience of English speakers will have access to the minority language or not. In the face of this dilemma media producers have taken several approaches: subtitling foreign speech, translating foreign speech through other characters, or leaving the language inaccessible except to those who use it. The additional difficulty with representing national sign languages is that both the language and the recording medium are visual. Sometimes, filmmakers make the choice of leaving some portions of the signed dialogue inaccessible to a non-signing hearing audience. On the one hand this choice could indicate a devaluing of the signed communication, as its specific content is considered irrelevant to the plot. On the other hand it could indicate that Deaf people have a right to be visible on television using their own language without accommodating hearing people. A number of choices made in the filming and editing can subtly undermine positive representations of Deaf ‘voices’ particularly to a Deaf audience. These choices often construct an image of sign languages as objectified, exoticised, disjointed, incomplete, or a code for spoken language. Simple choices such as using simultaneous speaking and signing by Deaf characters, cropping the scene, translating or not translating the dialogue have powerful implications for the ways that Deaf ‘voices’ are becoming more visible in the 21st century. Typical filming and editing conventions effectively silence the Deaf ‘voice.’ Over 20 years ago, in the comprehensive book, Hollywood Speaks: Deafness and the Film Entertainment Industry (1988), Schuchman’s complaint that the filming and editing techniques of the day often did not attend to preserving the visibility and comprehensibility of sign language eon the screen, still applies today. As editing techniques have evolved over the years, fr om reliance on wide and medium shots to frequent intercutting of close-ups, the tendency to cut sign language off the screen, and out of the comprehensible view of the audience, may have even increased. Recent Portrayals of Deaf People on Television During one television season in the United States between August 2006 and April 2007, 30 episodes of six different serial television programs portrayed signing Deaf characters. Three of these programs had on-going Deaf characters that appeared in a number of episodes throughout the season, while three other programs portrayed Deaf people in a one-off episode with a Deaf theme. Initial air date for the season Program and Season # of Episodes 1 14 Aug. 2006 Weeds, Season 2 5 2 20 Sep. 2006 Jericho, Season 1 13 3 28 Jan. 2007 The L Word, Season 4 9 Table 1. Dramas with Ongoing Deaf Characters during the 2006-2007 USA Television Season Initial air date Program, Season, Episode Episode Title 1 13 Dec. 2006 CSI: New York, Season 3, Episode 12 “Silent Night” 2 3 Apr. 2007 Law and Order: Criminal Intent, Season 6, Episode 18 "Silencer" 3 12 Apr. 2007 Scrubs, Season 6, Episode 16 “My Words of Wisdom” Table 2. One-off Episodes with Signing Deaf Characters during the 2006-2007 USA Television Seasons Ironically, although the shows with ongoing characters sometimes allow the Deafness of the character to be incidental to the character, it is only the one-off crime dramas that show Deaf people relating with one another as members of a vibrant community and culture based in sign language. Often, in the ongoing series, the characters remain isolated from the Deaf community and their interactions with other Deaf people are sparse or non-existent. For example, out of the 27 episodes with an ongoing Deaf character only two episodes of The L-Word have more than one Deaf character portrayed. In both Weeds and The L-Word the Deaf character is the love interest of one of the hearing characters, while in Jericho, the Deaf character is the sister of one of the main hearing characters. In these episodes though some of realities about Deaf people’s lives are touched on as they relate to the hearing characters, the reality of signing Deaf people’s social lives in the Deaf community is left absent and they are depicted primarily interacting with hearing people. The two episodes, from CSI: New York, and Law and Order: Criminal Intent, focus on the controversial theme of cochlear implants in the Deaf community. Though it is true that generally the signing Deaf community in the U.S.A. sees cochlear implants as a threat to their community, there is no record of this controversy ever motivating violent criminal acts or murder as portrayed in these episodes. In the episode of CSI: New York entitled “Silent Night” a conflict between a young Deaf man and Deaf woman who were formerly romantically involved is portrayed. The murdered young woman who comes from a Deaf family does not want her Deaf baby to have a cochlear implant while the killer ex-boyfriend who has a cochlear implant believes that it is the best option for his child. The woman’s Deaf parents are involved in the investigation. The episode of Law and Order: Criminal Intent, entitled “Silencer,” is also ultimately about a conflict between a Deaf man and a Deaf woman over cochlear implants. In the end, it is revealed that the Deaf woman is exploring the possibility of a cochlear implant. Her boyfriend projecting the past hurt of his hearing sister leaving him behind to go off and live her own life, doesn’t want his girlfriend to leave him once she gains more hearing. So he shoots the cochlear implant surgeon in the hand to prevent him from being able to perform the surgery. Then he accidentally kills him by crushing his voice box to prevent him from screaming. Analyzing Two Crime Dramas In both television dramas, the filmmakers use both sound and video editing techniques to mark the experiential difference between hearing and Deaf characters. In comparing the two dramas two techniques are evident : muting/distorting sounds and extreme close-ups on lips talking or hands signing. Though these techniques may heighten awareness of deaf experience to a non-signing audience they also point to a disabling stereotyping of the experience of being Deaf as lacking — framing their experience as hearing loss rather than Deaf gain (Bauman & Murray; Shakespeare 199). By objectifying sign language through extreme close ups American Sign Language is portrayed as something strange and unusual that separates Deaf signers from hearing speakers. The auditory silences can either jolt the hearing non-signer into awareness of the sensory aspect of sound that is missing or it can jolt them into awareness of the visual world that they often don’t really see. In the opening few scenes of the episodes both CSI: New York and Law and Order: Criminal Intent use sound editing alternately muting or distorting sounds as they cut between a ‘deaf’ auditory perspective and a ‘hearing’ perspective on the action as it unfolds. Even though the sound editing does play a part in the portrayal of Deaf people’s experience as lacking sound, the more important aspects of film production to attend to are the visual aspects where Deaf people are seen authentically signing in their own language. Scene Analysis Methodology In taking a closer look at a scene from each episode we can see exactly how the filming and editing techniques work to create an image of sign language. I have chosen comparable scenes where a Deaf individual is interviewed or interrogated by the police using a sign language interpreter. In each scene it can be assumed that all the communication is happening in both English and ASL through an interpreter, so at all times some signing should be occurring. In transcribing the scenes, I noted each point when the editor spliced different camera shots adjacent to each other. Because of the different visual aesthetics in each program where one relied heavily on continuous panning shots, I also noted where the camera shifted focus from one character to another marking the duration of screen time for each character. This allowed for a better comparison between the two programs. In my transcripts, I included both glosses of the ASL signs visible on the screen as well as the flow of the spoken English on the audio track. This enabled me to count how many separate shifts in character screen time segments contained signing and how much of these contained completely visible signing in medium shots. CSI:NY Witness Interview Scene In the first signing scene, Gina (played by Marlee Matlin) is brought in for an interview with Detective Taylor and a uniformed officer interpreter. The scene opens with a medium shot on Detective Taylor as he asks her, “What do you think woke you up?” The shot cuts to an extreme close up of her face and hands and pans to only the hands

Deaf and hard-of-hearing individuals use sign language as a means of communication. However, those around them, especially family members like the children of deaf adults, may face communication challenges if they are unfamiliar with sign language. This issue has prompted numerous researchers to conduct studies on sign language translation and recognition. However, there is currently no publicly available dataset specifically for Malaysian sign language. This article introduces an image dataset of the Malaysian Sign Language (MySL) hand gestures used in everyday situations. The dataset, named MyWSL2023, comprises 3,500 images of ten static Malaysian sign language words collected from five participants (two males and three females) aged between 20 and 21 years old. The data collection took place indoors under normal lighting conditions. The MyWSL2023 dataset, which has been made freely accessible to all researchers, serves as a valuable resource for not only investigating and developing automated systems for hearing-impaired and deaf individuals but also gesture and sign language recognition using vision-based methods. The dataset can be accessed for free at https://data.mendeley.com/datasets/zvk55p7ktd.

Over the past decade, there have been great advancements in radio frequency sensor technology for human–computer interaction applications, such as gesture recognition, and human activity recognition more broadly. While there is a significant amount of study on these topics, in most cases, experimental data are acquired in controlled settings by directing participants what motion to articulate. However, especially for communicative motions, such as sign language, such directed data sets do not accurately capture natural, in situ articulations. This results in a difference in the distribution of directed American Sign Language (ASL) versus natural ASL, which severely degrades natural sign language recognition in real‐world scenarios. To overcome these challenges and acquire more representative data for training deep models, the authors develop an interactive gaming environment, ChessSIGN, which records video and radar data of participants as they play the game without any external direction . The authors investigate various ways of generating synthetic samples from directed ASL data, but show that ultimately such data does not offer much improvement over just initialising using imagery from ImageNet. In contrast, an interactive learning paradigm is proposed by the authors in which model training is shown to improve as more and more natural ASL samples are acquired and augmented via synthetic samples generated from a physics‐aware generative adversarial network. The authors show that the proposed approach enables the recognition of natural ASL in a real‐world setting, achieving an accuracy of 69% for 29 ASL signs—a 60% improvement over conventional training with directed ASL data.

Sign Language Recognition (SLR) has become an essential area of research due to its potential to promote understanding between the deaf and hearing communities through communication. This paper provides an in-depth study of various methodologies and models employed in SLR, focusing on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). We analyze their application to datasets from various sign languages, such as Arabic Sign Language (ArSL), American Sign Language (ASL), and British Sign Language (BSL), and explore how these models improve the recognition of dynamic, multi-dimensional hand gestures. This research not only advances the understanding of deep learning applications in sign language recognition but also addresses critical challenges in data processing and real-time applications, paving the way for inclusive technologies in informatics and human-computer interaction. Despite the progress in applying deep learning techniques to SLR, several challenges remain, particularly in dataset limitations, handling large vocabularies, and ensuring consistent performance across diverse environments and signers. The paper also investigates the broader applications of SLR, such as virtual reality, healthcare, education, and accessibility, and discusses the integration of SLR with human-computer interaction systems. Furthermore, it highlights current limitations in the field, such as difficulties with video data handling, the need for standard datasets, and issues related to training computational models. Finally, the paper outlines future research directions, including developing more robust SLR systems that can function effectively in uncontrolled environments, improving data collection methodologies, and creating real-time, user-friendly applications to assist the community of deaf and hard-of-hearing individuals.

I BEGIN WITH A QUESTION: What does it mean to transliterate American Sign Language (ASL) and the visual realities of a Deaf life into creative texts written in English? This question is larger than the necessities of transliteration and conventions of print. If fiction writers or poets happen to be Deaf-meaning that they consider themselves to be members of the Deaf community and use ASL as their primary language-then they must also consider how writing in English (or other languages) displaces a cultural identity grounded in a visual-spatial language, one that has historically been denigrated, suppressed, and erased from sight. Indeed, it seems that even on the sentence level, written English resists the unsettling presence of transliteration across modalities; turn-taking in sign language cannot easily be slotted into conversation tags (e.g., he said, she said). A sign cannot be said. In English, dialogue without quotation marks is, in effect, speechless. Without the conventionalized use of quotation marks, dialogue shirts inward and inhabits an internal territory, for without quotations, what separates thought from conversation on the page? If one uses the conventions of written English, then the results are awkward and imply a one-to-one correlation between signs and English words (e.g., What's up? Dave signed). Tonal dialogue tags reveal problems with trying to sequence information on the page that, for a signer, would be perceived in a simultaneous, not linear, fashion (e.g., You should have gone to the meeting, she signed, disappointed). So, in effect, if one uses the conventions of printed English, then the story or poem embodies the hearing, not the Deaf, world. To evoke a resistant perspective, some Deaf writers and Deaf poets make use of code mixing, untranslated ASL gloss, and other hybrid forms that show the postcolonial possibilities for textualizing Deaf lives and sign language. It is important to remember that many Deaf people who are creative thinkers and innovative in their use of ASL simply do not bother with writing literary forms of English. Why should they? ASL has a long tradition of visual poetics and a rich traditional linguistic history with many genres-ASL poetry, narratives, jokes, and rap, to name just a few. With the advent of visual recording devices, ASL has its own form of print in that DVDs and videotapes can be watched repeatedly and disseminated to a larger audience. So, for the purposes of writing Deaf, why bother with English? It is, after all, the dreaded and always fragmented, incomplete language of speech therapy: words forced, unwieldy and thick, from mouths. There is also the uneven and sometimes humiliating experience of learning to read and write English, with its idiosyncratic orthography based, in many cases, upon phonetics. So much of English has to be heard in order to be understood and then used. As is the case with other diglossic subcultural groups, the use of written English, for many Deaf people, has been largely seen as utilitarian, a necessary tool for participation in U.S. social institutions. English is, after all, the language of government and business. More to the point, literary English and the attendant displays of hearing cultural norms do not reflect the lived, embodied realities of a Deaf person's life or sign language. Like Singer in Carson McCuIlers's The Heart Is a Lonely Hunter, the few deaf characters in literary English are most often written as metaphors for the human (i.e., hearing) condition. Historically, Deaf bodies have been pathologized, and their ears, as one specific site of conflict, have been colonized through attempts to fix deafness. The deafened ear, the silenced body, and endangered soul have all been mapped out by centuries of medical and religious literature. In the long campaign against sign language, Deaf people have been told to sit on their hands and speak up, have been ordered not to marry each other, and, to this day, continue to suffer the effects of an imperialist and audist directive to conform, to speak, and to not unsettle or disrupt the ongoing narratives of the hearing world. …

In the course of our research work, the American, Russian and Turkish sign languages were analyzed. The program of recognition of the Kazakh dactylic sign language with the use of machine learning methods is implemented. A dataset of 5000 images was formed for each gesture, gesture recognition algorithms were applied, such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, while two data types were combined into one database, which caused a change in the architecture of the system as a whole. The quality of the algorithms was also evaluated. The research work was carried out due to the fact that scientific work in the field of developing a system for recognizing the Kazakh language of sign dactyls is currently insufficient for a complete representation of the language. There are specific letters in the Kazakh language, because of the peculiarities of the spelling of the language, problems arise when developing recognition systems for the Kazakh sign language. The results of the work showed that the Support Vector Machine and Extreme Gradient Boosting algorithms are superior in real-time performance, but the Random Forest algorithm has high recognition accuracy. As a result, the accuracy of the classification algorithms was 98.86 % for Random Forest, 98.68 % for Support Vector Machine and 98.54 % for Extreme Gradient Boosting. Also, the evaluation of the quality of the work of classical algorithms has high indicators. The practical significance of this work lies in the fact that scientific research in the field of gesture recognition with the updated alphabet of the Kazakh language has not yet been conducted and the results of this work can be used by other researchers to conduct further research related to the recognition of the Kazakh dactyl sign language, as well as by researchers, engaged in the development of the international sign language

A major challenge faced by our society is the difficulty individuals with disabilities encounter in expressing their emotions to others. People with disabilities often depend on sign (gesture) languages for communication. This project aims to develop a model capable of recognizing and translating sign language alphabets from hand gestures into text and audio. The primary objective is to enhance communication between individuals with hearing impairments and the broader society. We assessed our model's performance using a publicly accessible Indian Sign Language (ISL) dataset. For image classification, we employed Convolutional Neural Networks (CNNs) with the Inception V3 architecture. Hand gestures are captured through webcams, and our model identifies the corresponding alphabet for each gesture. This project seeks to overcome current challenges in Sign Language Recognition and aims to enhance its effectiveness and efficiency.

Gesture recognition and interpretation systems broadly implement deep learning architectures to facilitate human system interaction, including communication through sign language. This paper deals with the automatic interpretation of American Sign Language (ASL) gestures using computer vision with image processing. A hybrid SqueezeNet Multi-Lane CapsNet architecture is introduced where the features are learnt by a SqueezeNet convolutional architecture and the relative pose estimation of the features is carried out by capsules in multiple lanes. Multi-Lane CapsNet setup ensures parallel identification of distinct features that are then mapped by using a dynamic routing algorithm. Transfer learning was implemented on the SqueezeNet model with weights from pre-training the ImageNet dataset. This ensures optimized initialization of weights and reduced time for convergence during training. The hybrid architecture is trained on a benchmark dataset containing hand gestures pertaining to the English alphabets from ASL convention. The SqueezeCapsNet model performed with a high accuracy of 91.6%, a false positive rate of 0.0033%, and a comparative analysis of this model is presented with the existing state-of-the-art ASL gesture recognition models. The weights from the SqueezeNet model were extracted to present a Grad-CAM++ visualization of the gesture interpretation system.

Physical disability is one of the factor in human beings, which cannot be ignored. A person who can’t listen by nature is called deaf person. For the representation of their knowledge, a special language is adopted called ‘Sign-Language’. American Sign Language (ASL) is one of the most popular sign language that is used for learning process in deaf persons. For the representation of their knowledge by deaf persons, a special language is adopted ‘Sign-Language’. American Sign Language contains a set of digital images of hands in different shapes or hand gestures. In this paper, we present feature based algorithmic analysis to prepare a significant model for recognition of hand gestures of American Sign Language. To make a machine intelligent, this model can be used to learn efficiently. For effective machine learning, we generate a list of useful features from digital images of hand gestures. For feature extraction, we use Matlab 2018a. For training and testing, we use weka-3-9-3 and Rapid Miner 9 1.0. Both application tools are used to build an effective data modeling. Rapid Miner outperforms with 99.9% accuracy in auto model.