Sign Language Web Pages

소리가 잘 들리지 않는 청각장애인은 소리를 표시하는 글자를 익히고 복잡한 개념을 전달하는 글을 이해하는 데 어려움이 많다. 그래서 자연스럽게 표정, 몸짓, 손짓으로 의사를 전달하는 수화가 청각장애인들의 주요 의사소통수단으로 자리잡아왔지만 사회에서는 글과 말이 주요 정보전달 수단으로 이용되고 있어 청각장애인이 정보에 접근하고 지식을 넓혀 직업을 갖는데 큰 어려움이 있다. 특히 근래에 인터넷이 보편화됨에 따라 정상인의 정보 습득량은 크게 늘었지만, 글로 표현된 인터넷을 이해하기 어려운 청각장인들은 인터넷 정보에 접근하는데 어려움이 많다. 본 연구에서는 청각장애인을 위한 수화 웹페이지를 제작할 수 있는 수화 웹페이지 제작시스템을 개발하였다. 수화 웹페이지 제작 시스템은 청각장애인용 수화 웹페이지 제작자가 통상적인 인터넷 사용에 필요한 하드웨어 및 소프트웨어 환경만 갖추면 운영할 수 있도록 웹기반으로 개발하였다. 수화용 웹 페이지 제작자는 게시판 형태의 인터페이스를 이용하여 변역하고자하는 문장을 서버에 전송할 수 있다. 서버는 수신된 문장을 수화로 번역하여 3D 아바타 기반의 수화 그래픽 애니메이션을 랜더링한다. 수화 그래픽 애니메이션은 MP4 형식의 동영상으로 변환되며, 스트리밍 서버의 저장소에 보관된다. 저장된 수화 동영상의 이름과 주소는 수화 웹페이지 제작자가 처음 문장을 입력하였던 게시판에 표시되도록 하여 향후 청각장애인용 웹 페이지를 제작할 때 활용할 수 있도록 하였다. 또한 본 연구에서는 웹페이지 제작자가 공공기관의 웹페이지를 제작할 수 있도록, 이들 기관에서 현재 사용된 글들을 수화로 번역할 때 필요한 수화 단어를 수화 웹페이지 제작 시스템의 수화 사전에 추가하였다. 수화 단어를 추가함으로써 웹기반 수화 웹페이지 제작 시스템이 공공부문에 보다 더 많이 활용될 수 있을 것이다. Hearing-impaired people have difficulty in hearing, so it is also hard for them to learn letters that represent sound and text that conveys complex and abstract concepts. Therefore it has been natural choice for the hearing-impaired people to use sign language for communication, which employes facial expression, and hands and body motion. However, the major communication methods in daily life are text and speech, which are big obstacles for the hearing-impaired people to access information, to learn and make intellectual activities, and to get jobs. As delivering information via internet become common the hearing-impaired people are experiencing more difficulty in accessing information since internet represents information mostly in text forms. This intensifies unbalance of information accessibility. This paper reports web-based text-to-sign language translating system that helps web designer to use sign language in web page design. Since the system is web-based, if web designers are equipped with common computing environment for internet browsing, they can use the system. The web-based text-to-sign language system takes the format of bulletin board as user interface. When web designers write paragraphs and post them through the bulletin board to the translating server, the server translates the incoming text to sign language, animates with 3D avatar and records the animation in a MP4 file. The file addresses are fetched by the bulletin board and it enables web designers embed the translated sign language file into their web pages by using HTML5 or Javascript. Also we analyzed text used by web pages of public services, then figured out new words to the translating system, and added to improve translation. This addition is expected to encourage wide and easy acceptance of web pages for hearing-impaired people to public services.

Googling for Health Information

Sign Languages (SLs) are employed by deaf and hard-of-hearing (DHH) people to communicate on a daily basis. However, the communication with hearing people still faces some barriers, mainly because of the scarce knowledge about SLs among hearing people. Hence, tools to allow the communication between users of either sign or spoken languages must be encouraged. A stepping stone in this direction is the research of the sign language translation (SLT) task, which aims to produce a spoken language translation of a sign language video or vice versa. By implementing these types of translators in portable devices, we will make considerable progress towards a barrier-free communication between DHH and hearing people. That is why, in this work, we focus on reviewing the literature on SLT and provide the necessary background about SLs. Besides, we summarise the available datasets and the results found in the literature for one of the most used datasets, the RWTH-PHOENIX-2014T. Moreover, the survey lists the challenges that need to be tackled within the SLT research and also for the adoption of SLT technologies, and proposes future research lines.

Deaf people across many parts of the world recount stories of 'surviving' their education because of the lack of access to the curriculum via a sign language on the one hand, and on the other, because of the intense suppression of sign languages that existed for many decades in many countries. Indeed, in many places, this remains the status quo today. In the quote that opens this chapter, we have access to the views of Helga Stevens, who in 2014 became the first deaf woman to be elected to the European Parliament. She is also a lawyer who has served as Executive Director and, later, President of the European Union of the Deaf (EUD), and as a parliamentarian and a senator in Flanders, Belgium. As a sign language user, it is clear that she is pushing the boundaries for ever-increasing recognition and use of sign languages across the gamut of human interaction. Indeed, she was the central public figure behind the move to have Flemish Sign Language legally recognised in Flanders in 2006 (Wheatley & Pabsch, 2010). In this chapter, our focus is on where and how deaf people use sign languages in everyday life and for what purposes, with concentration in the latter part of the chapter on various institutional contexts where people communicate directly in sign language or indirectly through mediated interpretation.

David Quinto-Pozos: Preface Laurence Leonard: Foreword 1. David Quinto-Pozos: Introduction: Considering Communication Disorders and Differences in the Signed Language Modality Section: Developmental Language Disorders in the Signed Modality 2. Rosalind Herman, Katherine Rowley, Chloe Marshall, Kathryn Mason, Joanna Atkinson, Bencie Woll & Gary Morgan: Profiling SLI in Deaf Children who are Sign Language Users 3. David Quinto-Pozos, Jenny Singleton, Peter Hauser, & Susan Levine: A Case-Study Approach to Investigating Developmental Signed Language Disorders 4. Aaron Shield & Richard P. Meier: The Acquisition of Sign Language by Deaf Children with Autism Spectrum Disorder 5. Wolfgang Mann & Tobias Haug: Mapping Out Guidelines for the Development and Use of Sign Language Assessments - some Critical Issues, Comments, and Suggestions Section: Fluency Disorders, Neurogenics and Acquired Communication Disorders 6. Geoffrey Whitebread: A Review of Stuttering in Signed Languages 7. Martha Tyrone: Sign Dysarthria: A Speech Disorder in Signed Language 8. Patricia Spanjer, Marielle Fieret & Anne Baker: The Influence of Dementia on Language in a Signing Population 9. Anne E. Baker and Beppie van den Bogaerde: KODAs: A Special Form of Bilingualism 10. Deborah Chen Pichler, James Lee and Diane Lillo-Martin: Language Development in ASL-English Bimodal Bilinguals

Deaf or mute persons frequently use sign language to communicate, but it takes a lot of practice to learn. Principal mode of communication of the Hard-of-Hearing and Deaf community is sign language. Autism, Apraxia of speech, Cerebral Palsy, and Down syndrome are just a few of the disorders that may benefit from sign language. We're utilizing ASL (American Sign Language) for this project. Although ASL uses the same alphabet as English, it is not a dialect of English. American Sign Language is a separate language with its own linguistic framework. Signs are not expressed in the same order as words are in English. This is due to sign language's distinct grammar and visual aspect. In the United States, around half a million people use ASL. We will develop and implement a mobile application that will serve as a translation system to enable people communicate more efficiently using signs in this project. We will demonstrate a real-time sign language recognition system that uses existing datasets to transform a video of a user's signs into text.

Projections show the numbers of deaf and deafblind individuals are quickly increasing, as both the number of deaf and blind individuals are expected to double within the next 50 years. Additionally, there is limited support for deafblind individuals, as they have little sensory access to the world. Most existing solutions are inadequate because they are either not portable, readily accessible or are very expensive. The goal of our project is to create a low cost, accessible solution for those with these disabilities: a portable device that utilizes the sense of touch to teach sign language, instead of conventional sign language learning methods that rely on sight. By doing this, a more immersive and personalized experience is created. The implementation of this solution is two-pronged: first, the physical portion of the solution is equipped with servo motors that control the pulling and release of a cord threaded through rings on the fingers that moves the user's hands into various sign language positions, which is closely modeled after human hand anatomy. Secondly, feasibility is determined for an AI algorithm to take in data from an external camera to efficiently add new signs to the glove, and also to track the user's sign language patterns and rate the user's accuracy with various signs. Through these mechanisms the user is engaged in learning new signs and expanding their sign language vocabulary, all without their sense of sight. We were successful in creating a working prototype analyzed through hand-tracking mechanisms, and achieving a 91-92% accuracy for the American Sign Language alphabet.

Health information about inherited forms of cancer and the role of family history in cancer risk for the American Sign Language (ASL) Deaf community, a linguistic and cultural community, needs improvement. Cancer genetic education materials available in English print format are not accessible for many sign language users because English is not their native or primary language. Per Center for Disease Control and Prevention recommendations, the level of literacy for printed health education materials should not be higher than 6th grade level (~ 11 to 12years old), and even with this recommendation, printed materials are still not accessible to sign language users or other nonnative English speakers. Genetic counseling is becoming an integral part of healthcare, but often ASL users are not considered when health education materials are developed. As a result, there are few genetic counseling materials available in ASL. Online tools such as video and closed captioning offer opportunities for educators and genetic counselors to provide digital access to genetic information in ASL to the Deaf community. The Deaf Genetics Project team used a bilingual approach to develop a 37-min interactive Cancer Genetics Education Module (CGEM) video in ASL with closed captions and quizzes, and demonstrated that this approach resulted in greater cancer genetic knowledge and increased intentions to obtain counseling or testing, compared to standard English text information (Palmer et al., Disability and Health Journal, 10(1):23-32, 2017). Though visually enhanced educational materials have been developed for sign language users with multimodal/lingual approach, little is known about design features that can accommodate a diverse audience of sign language users so the material is engaging to a wide audience. The main objectives of this paper are to describe the development of the CGEM and to determine if viewer demographic characteristics are associated with two measurable aspects of CGEM viewing behavior: (1) length of time spent viewing and (2) number of pause, play, and seek events. These objectives are important to address, especially for Deaf individuals because the amount of simultaneous content (video, print) requires cross-modal cognitive processing of visual and textual materials. The use of technology and presentational strategies is needed that enhance and not interfere with health learning in this population.

We describe the development of a computational cognitive model that explains navigation behavior on the World Wide Web. The model, called SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT cognitive architecture), is motivated by Information Foraging Theory (IFT), which quantifies the perceived relevance of a Web link to a user's goal by a spreading activation mechanism. The model assumes that users evaluate links on a Web page sequentially and decide to click on a link or to go back to the previous page by a Bayesian satisficing model (BSM) that adaptively evaluates and selects actions based on a combination of previous and current assessments of the relevance of link texts to information goals. SNIF-ACT 1.0 utilizes the measure of utility, called information scent, derived from IFT to predict rankings of links on different Web pages. The model was tested against a detailed set of protocol data collected from 8 participants as they engaged in two information-seeking tasks using the World Wide Web. The model provided a good match to participants' link selections. In SNIF-ACT 2.0, we included the adaptive link selection mechanism from the BSM that sequentially evaluates links on a Web page. The mechanism allowed the model to dynamically build up the aspiration levels of actions in a satisficing process (e.g., to follow a link or leave a Web site) as it sequential assessed link texts on a Web page. The dynamic mechanism provides an integrated account of how and when users decide to click on a link or leave a page based on the sequential, ongoing experiences with the link context on current and previous Web pages. SNIF-ACT 2.0 was validated on a data set obtained from 74 subjects. Monte Carlo simulations of the model showed that SNIF-ACT 2.0 provided better fits to human data than SNIF-ACT 1.0 and a Position model that used position of links on a Web page to decide which link to select. We conclude that the combination of the IFT and the BSM provides a good description of user-Web interaction. Practical implications of the model are discussed.

The World Wide Web is growing at an uncontrollable rate. Hundreds of thousands of web sites appear every day, with the added challenge of keeping the web directories up-to-date. Further, the uncontrolled nature of web presents difficulties for Web page classification. As the number of Internet users is growing, so is the need for classification of web pages with greater precision in order to present the users with web pages of their desired class. However, web page classification has been accomplished mostly by using textual categorization methods. Herein, we propose a novel approach for web page classification that uses the HTML information present in a web page for its classification. There are many ways of achieving classification of web pages into various domains. This paper proposes an entirely new dimension towards web page classification using Artificial Neural Networks (ANN). Index Terms: World Wide Web, Web page classification, textual categorization, HTML, Artificial Neural Networks, ANN.

Sign languages are natural languages in the visual domain. Because they lack a written form, they provide a sharper tool than spoken languages for investigating lexicality effects which may be confounded by orthographic processing. In a previous study, we showed that the neural networks supporting phoneme monitoring in deaf British Sign Language (BSL) users are modulated by phonology but not lexicality or iconicity. In the present study, we investigated whether this pattern generalizes to deaf Swedish Sign Language (SSL) users. British and SSLs have a largely overlapping phoneme inventory but are mutually unintelligible because lexical overlap is small. This is important because it means that even when signs lexicalized in BSL are unintelligible to users of SSL they are usually still phonologically acceptable. During fMRI scanning, deaf users of the two different sign languages monitored signs that were lexicalized in either one or both of those languages for phonologically contrastive elements. Neural activation patterns relating to different linguistic levels of processing were similar across SLs; in particular, we found no effect of lexicality, supporting the notion that apparent lexicality effects on sublexical processing of speech may be driven by orthographic strategies. As expected, we found an effect of phonology but not iconicity. Further, there was a difference in neural activation between the two groups in a motion-processing region of the left occipital cortex, possibly driven by cultural differences, such as education. Importantly, this difference was not modulated by the linguistic characteristics of the material, underscoring the robustness of the neural activation patterns relating to different linguistic levels of processing.

This paper presents the development of a real-time sign language translator that integrates speech recognition technology to bridge communication gaps between sign language users and non-signers. The system translates spoken or typed words into corresponding American Sign Language (ASL) and Indian Sign Language (ISL) gestures using a predefined database of sign representations. The paper discusses the system’s architecture, implementation challenges, results, and potential applications in education, public spaces, and assistive technology. Additionally, the research explores the integration of AI-driven enhancements to improve translation accuracy, accommodate a broader vocabulary, and incorporate non-manual sign components such as facial expressions and body language. Future work will focus on refining gesture recognition, improving speech processing, and ensuring accessibility across diverse linguistic and cultural contexts.

In Brazil, there is a considerable number of deaf people, sign language users, who should have access to equal opportunities. However, sign language is unknown by the most Brazilian population, especially those who work in public institutions, and deny the deaf their rights to equality. Therefore, several professionals, among them, the municipal guards, are not prepared for the diversity. to investigate the knowledge of the Municipal Guards (MG) from Curitiba about the deaf, Brazilian Sign Language (Libras),and their service practices when they need to interact with deaf citizens. It´s a mixed crosscut study; data were collected by the application of a semi-structured questionnaire answered by 50 municipal guards, with questions on their knowledge about the deaf, the sign language and the service practices conducted by their corporation towards deaf people. Data were analyzed quantitatively and qualitatively by means of Bardin's Content Analysis. The results demonstrate that participants have a limited knowledge of deafness and Libras. Most members of the Municipal Guard have a view of deaf people and the Brazilian Sign Language based on an organic perspective of deafness, and it was also observed that during their service practices towards deaf people, many of them could not either interact or communicate with them.

Sign Language Detection project was just a step to real-time system that will be able to understand American Sign Language (ASL) hand gestures and convert them into a written language for the hearing-impaired community. This was a machine learning project that utilized the Random Forest and Support Vector Classifier (SVC) algorithms to obtain the highest accuracy for the classification of sign language gestures. Media Pipe hand landmarks can now be extracted, normalized, preprocessed and even the most important features of the gestures can be focused on. Ensemble learning which is a combination of the complementary strengths of the algorithms is one of the main reasons for the system to have robust classification. Besides the good performance and a variety of accuracy metrics such as precision, recall and confusion matrices, the system is also quite reliable in different gesture classes. The system with all its features such as real-time video processing and instant feedback can be used in educational, healthcare and social settings. The project will be the one that can bridge the communication gap existing between sign language users and the hearing world by going a step further in assisting the community in becoming more inclusive and accessible to the hearing-impaired."

Web Crawlers ‘browse’ the World Wide Web (WWW) on behalf of search engine, to collect web pages from numerous collections of billions of documents. Metacrawler is similar to that of a meta search engine that combines the top web search results from popular search engines. World Wide Web is growing rapidly. This possesses great challenges to general purpose crawlers. This paper introduces an architectural framework of a Metacrawler. This crawler enables the user to retrieve information that is relevant to the topic from more than one traditional web search engines. The crawler works in such a way that it fetches only the pages that are relevant to the topic. The PageRank algorithm is often used in ranking web pages. But, the ranking causes the problem of topic-drift. So, modified PageRank algorithm is used to rank the retrieved web pages in such a way that it reduces this problem. The clustering method is used to combine the search results so that the user can easily select web pages from the clustered results based upon the requirement. Experimental results show the effectiveness of the Metacrawler.