Interpretation Of Signs Research Articles

Alphabet based Sign Language Translator for Two Way Communication

Alphabet based Sign Language Translator for Two Way Communication

Tamil Sign Language Translation and Recognition System for Deaf-mute People Using Image Processing Techniques

This work concentrates on the device, which helps as a translation system for translating sign gestures into text. The disabled people, in particular, hearing and speech impaired people, are facing difficulties in society. Communication of disabled people becomes worse as the majority of ordinary people do not understand it. These disabled people face difficulty communicating with others; some have many problems communicating with others in sign language. It causes the communication gap between them where impaired people cannot share their views and skills with others. We headed to facilitate communication between the disabled people and the "Tamil sign language translator to solve this problem." Here, gestures are translated to Tamil language to find a localized solution. It processes 31 Tamil alphabets, 12 Vowels, 18 Consonants, and 1 Aayudha Ezhuthu. It is 32 combinations with five fingers points either up or down and mapped to decimal numbers. Here in this process, we need edge detection, which is accurately done and Processed by canny edge detection. In addition to this process, we have used two gesture recognition methods and training the input system through our mainframe algorithm called scale-invariant feature detection transform. We have developed this system, which is useful for deaf and dumb people for essential communication.

Deaf professionals’ perceptions of 'trust' in relationships with signed language interpreters

The concept of ‘trust’ is frequently used when discussing the working relationship between deaf signers and signed language interpreters, with interpreters often claiming that trust is a prerequisite to a successful interaction. This paper presents original data from an in-depth research project which used collaborative autoethnography to gather the experiences of seven deaf academics who work regularly with British Sign Language (BSL) interpreters, who interpret between BSL and spoken English, to analyse the concept of ‘trust’ in our working relations with BSL interpreters. We found that ‘trust’ is not a useful or productive concept for our interpersonal and professional aims. Instead, we outline multiple ways in which deaf academics can assess and evaluate interpreters’ values, competencies, and performance without relying on ‘trust’. Our findings provide an important, powerful and under-explored perspective on the working relations between deaf academics and interpreters. We suggest these findings can be applied by deaf BSL signers and interpreters in contexts beyond academia, and constitute an important contribution to the literature on interpreting.

Application of Machine Learning in Malaysia Sign Language Translation

Sign language serves as a vital form of communication for individuals with hearing impairments, enabling seamless interaction among those who cannot hear. It is a widely used means of communication worldwide, facilitating communication within the deaf community. In Malaysia, Malaysian Sign Language (MySL) prevails as the primary sign language employed by the deaf community. However, sign languages possess unique grammatical rules and structures, making them unfamiliar to hearing individuals, leading to potential misunderstandings and communication barriers. This project aimed at developing a sign language translator capable of translating 24 alphabets based on hand gestures. The system employs a dataset of sign language alphabet images, gathered and trained using the Teachable Machine. To evaluate the translator's performance, response time is thoroughly analyzed. The results indicate that 18 out of the 24 alphabets can be recognized within 5 seconds, displaying promising accuracy. By bridging the communication gap between deaf and hearing individuals, the findings of this study hold substantial potential to enhance interactions and foster better understanding between these two communities. The sign language translator represents a significant step towards inclusive communication and improved accessibility for individuals with hearing impairments

Discourse on the Civil Rights of the Deaf People in Making a Notary Deed

It is generally said that disabled persons do not have proper access to justice and are not treated equally before the law. In Indonesia, the human rights approach has been incorporated into developing laws and policies for disabled persons. However, disabled people still face legal difficulties. This article aims to discuss the legal capacity of a deaf person to perform the legal action of making a deed before a notary in Indonesia from the perspectives of human rights, contracts, and notary laws. It reflects legal research that uses a statutory and conceptual approach. Primary legal materials are obtained from the national laws and regulations of Indonesia and relevant international legal instruments, while secondary legal materials are obtained from books, journal articles, and website content. The findings reveal that Indonesian national law generally recognizes the right of persons with disabilities, including deaf people, to perform civil law actions. In general, deaf people can sign a contract; however, there is no guarantee that they understand the communication process in making a deed. In this context, there are still obstacles related to the lack of sign language interpreters in Indonesia, which can complicate the transferring of information when making notarial deeds between deaf clients, notaries, and witnesses.

PENGEMBANGAN APLIKASI PENERJEMAH BAHASA ISYARAT INDONESIA (BISINDO) MENGGUNAKAN METODE LONG-SHORT TERM MEMORY

The Indonesian Sign Language Translator (BISINDO) application is intended for individuals who are deaf and mute assist them in communicating with normal people. Due to the limitations of technology to assist individuals who are deaf and mute, the Indonesian Sign Language Translator application was developed. Additionally, it is hoped that this application will enable individuals who are deaf and mute to learn Indonesian Sign Language in real-time. LSTM generally consists of a cell, input, gate, and forget gate. LSTM is highly suitable for classifying, processing, and making predictions based on time series data. In this research, hand landmark recognition is used as a medium to store finger gestures from user inputs, which will then be saved in the form of coordinate points and stored in a file in csv format. Subsequently, this file will be trained and called back to determine the output.

Network Translation Criticism Model from the Perspective of Communication Studies

In this era of the Internet, criticism of network translation is rising gradually. The criticism of network translation is not only a communication tool, but also closely related to all aspects of translation. This paper takes Chinese-English Dictionary of Chengdu 2021 FISU World University Games Publicsigns as an example, combined with the characteristics of network communication, studies a series of discussions on the network before and after its publication, and analyzes Chinese-English Dictionary of Chengdu 2021 FISU World University Games Publicsigns. This paper discusses the characteristics and methods of the translation of public signs from Chinese to English, the great role of the network translation criticism mode in translation and the enlightenment it brings to the development of Chinese culture.

Mixed Abilities and Varied Experiences: A Group Autoethnography of a Virtual Summer Internship

The COVID-19 pandemic forced many people to convert their daily work lives to a "virtual" format, in which they connected remotely from home. In this new, virtual environment, accessibility barriers changed, in some respects for the better (e.g., more flexibility) and in other aspects, for the worse (e.g., problems including American Sign Language interpreters over video calls). Microsoft Research held its first cohort of all virtual interns in 2020. We, the interns, full-time members, and affiliates of the Ability Team, a Microsoft research team focused on accessibility, report on our experiences navigating the challenges of working remotely. We constituted a variety of abilities, positions, and levels of seniority. Using an autoethnographic method, we provide a nuanced view of how the virtual setting affected the experiences of our mixed-ability team, the strategies we used to improve access, and areas for further improvement. We close by presenting guidelines for future virtual mixed-ability teams to improve workplace accessibility.

An end-to-end framework for translation of American sign language to low-resource languages in Nigeria

Deaf and hard of hearing (DHH) is a communication impairment that affects millions of people worldwide, hindering communication through the vocal cavity and requiring the use of sign language. This communication gap often leads to limited access to education and job opportunities. While artificial intelligence (AI)-driven technologies have been investigated to address this issue, there has been no study focusing on the intelligent and automatic translation of American sign gestures to text in low-resource languages (LRL) specifically in Nigeria. To address this gap, we propose a novel end-to-end framework for translating American Sign Language (ASL) to LRLs such as Hausa, Ibo, and Yoruba in Nigeria. Our framework employs a Transformer-based model for ASL-to-Text generation and a translation model known as no language left behind for translating the generated text from English to LRL. We evaluated the performance of the ASL-to-Text model using BLEU scores, which resulted in BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of 18.55, 8.99, 4.99, and 2.96, respectively. Qualitative analysis of the framework shows that the participants were able to comprehend the translated text and were also satisfied with both the ASL-to-Text and Text-to-LRL models. Our proposed framework demonstrates the potential for AI-driven technologies to foster inclusivity in sociocultural interactions and education, especially for people with DHH in low-resource settings.

The System of the Educational Process of Pupils with Disabilities in Ukraine

This expert text brings thought and reflection to the system of special pedagogy in Ukraine. We hear about this country in the context of a war conflict, and other equally important areas are unknown to us. At the present stage, new perspectives have shown up in the system of correctional education related to the rethinking of the attitude to the organisation of education and upbringing of children with special needs. Authorities, pedagogues, and ordinary citizens seek to come closer to understanding the difficulties these individuals face; actively discuss the benefits of special or integrated education; try to create certain conditions for their life (ramps, sign language translation, etc.); implement foreign experience. The practice of European countries, the United States, and Canada shows that children with special needs can be educated not only in special, separate from general education institutions, but also in usual schools on the model of integrated or inclusive education, which provides quality educational services to children regardless level of their development. The introduction of modern models of education, upbringing, and rehabilitation of children with psychophysical development requires not only a special organisation of the material environment, but also training of psychological and pedagogical staff of secondary schools, as the effectiveness of integrated and inclusive education depends on proper psychological and pedagogical support of these children. Non-governmental organisations, including organisations of parents of children with special educational needs, play a significant role in implementing such changes in Ukrainian society.

Development of a hybrid word recognition system and dataset for the Azerbaijani Sign Language dactyl alphabet

The paper introduces a real-time fingerspelling-to-text translation system for the Azerbaijani Sign Language (AzSL), targeted to the clarification of the words with no available or ambiguous signs. The system consists of both statistical and probabilistic models, used in the sign recognition and sequence generation phases. Linguistic, technical, and human–computer interaction-related challenges, which are usually not considered in publicly available sign-based recognition application programming interfaces and tools, are addressed in this study. The specifics of the AzSL are reviewed, feature selection strategies are evaluated, and a robust model for the translation of hand signs is suggested. The two-stage recognition model exhibits high accuracy during real-time inference. Considering the lack of a publicly available dataset with the benchmark, a new, comprehensive AzSL dataset consisting of 13,444 samples collected by 221 volunteers is described and made publicly available for the sign language recognition community. To extend the dataset and make the model robust to changes, augmentation methods and their effect on the performance are analyzed. A lexicon-based validation method used for the probabilistic analysis and candidate word selection enhances the probability of the recognized phrases. Experiments delivered 94% accuracy on the test dataset, which was close to the real-time user experience. The dataset and implemented software are shared in a public repository for review and further research (CeDAR, 2021; Alishzade et al., 2022). The work has been presented at TeknoFest 2022 and ranked as the first in the category of social-oriented technologies.

Detection and Interpretation of Indian Sign Language Using LSTM Networks

Detection and Interpretation of Indian Sign Language Using LSTM Networks

American Sign Language Recognition and Generation : A CNN-based Approach

Although not a global language, sign language is an essential tool for the deaf community. Communication between these communities and hearing population is severely hampered by this, as human-based interpretation can be both costly and time-consuming. In this paper, we present a real-time American Sign Language (ASL) generation and recognition system that makes use of Convolutional Neural Networks and deep learning (CNNs). Despite differences in lighting, skin tones, and backdrops, our technology is capable of correctly identifying and generating ASL signs. We trained our model on a large dataset of ASL signs in order to obtain a high level of accuracy. Our findings show that, with accuracy rates of 98.53% and 98.84%, respectively, our system achieves high accuracy rates in both training and validation. Our approach uses the advantages of CNNs to accomplish quick and precise recognition of individual letters and words, making it particularly effective for sign fingerspelling recognition. We believe that our technology has the ability to transform communication between the hearing community and the deaf and hard-of-hearing communities by providing a dependable and cost-effective way of sign language interpretation. Our method could help people who use sign language communicate more easily and live better in a range of environments, including schools, hospitals, and public places.

Savitar: an intelligent sign language translation approach for deafness and dysphonia in the COVID-19 era

PurposeIn the COVID-19 era, sign language (SL) translation has gained attention in online learning, which evaluates the physical gestures of each student and bridges the communication gap between dysphonia and hearing people. The purpose of this paper is to devote the alignment between SL sequence and nature language sequence with high translation performance.Design/methodology/approachSL can be characterized as joint/bone location information in two-dimensional space over time, forming skeleton sequences. To encode joint, bone and their motion information, we propose a multistream hierarchy network (MHN) along with a vocab prediction network (VPN) and a joint network (JN) with the recurrent neural network transducer. The JN is used to concatenate the sequences encoded by the MHN and VPN and learn their sequence alignments.FindingsWe verify the effectiveness of the proposed approach and provide experimental results on three large-scale datasets, which show that translation accuracy is 94.96, 54.52, and 92.88 per cent, and the inference time is 18 and 1.7 times faster than listen-attend-spell network (LAS) and visual hierarchy to lexical sequence network (H2SNet) , respectively.Originality/valueIn this paper, we propose a novel framework that can fuse multimodal input (i.e. joint, bone and their motion stream) and align input streams with nature language. Moreover, the provided framework is improved by the different properties of MHN, VPN and JN. Experimental results on the three datasets demonstrate that our approaches outperform the state-of-the-art methods in terms of translation accuracy and speed.

Special issue on sign language translation and avatar technology

Special issue on sign language translation and avatar technology

A comparative analysis of Indian sign language recognition using deep learning models

Sign language is a form of communication where people use bodily gestures, particularly those of hands and arms. This method of communication is put into motion when spoken communication is unattainable or disfavored. There are very few people who can translate sign language and readily understand them. It would be convenient for the hearing-impaired to have a platform where their sign language could be translated easily. Hence, through this study, with the help of artificial neural networks, we wish to compare how various widely implemented deep learning architectures respond to faultless translation of Indian sign language for the native audience. This research would streamline the development of software tools that can accurately predict or translate ISL. For the purpose of understanding the method of training the machine and exploring our model’s performance without any optimizations, a Convolutional Neural Network architecture was implemented. Over the course of our research, there have been several Pre-trained Transfer Learning Models implemented that have yielded promising results. The research aims to contrast how various convolutional neural networks perform while translating Indian Sign Actions on a custom dataset that factors in illumination, angles, and different backgrounds to provide a balanced and distinctive set of images. The goal of this study is to make clear comparisons between the various deep learning frameworks. Hence, a fresh Indian sign language dataset is introduced. Since every dataset in the field of deep learning has special properties that may be utilized for the betterment of the existing models, the development of a fresh dataset could be viewed as a development in the field. The optimum model for our task: classification of these gestures is found to be ResNet-50 (Accuracy = 98.25% and F1-score = 99.34%), and the least favorable was InceptionNet V3 (Accuracy = 66.75%, and F1-score = 70.89%).

Sign Language Detection and Translation

Communication between the general public and the deaf community is difficult. Most people find it difficult to communicate without an interpreter since sign language is not universally understood. This research suggests utilizing machine learning methods to build an effective Sign Language Detection and Translation model employing real-time dataset. This model could be employed in school and other places, facilitating communication between impaired and non-impaired people. The suggested approach can be used to recognize sign language with ease using Keras and Tensorflow.

Nouvelles perspectives dans l’interprétation du signe mélodique en forme de V dans le papyrus musical de Ménandre (P.Oxy. LIII 3705)

Abstract With the fourteen musical papyri that have been discovered in situ, all dating from the Roman period, the city of Oxyrhynchus, in Middle Egypt, constitutes the largest reservoir of ancient “scores” found up to now. By their fragmentary appearance, these papyri are often difficult to interpret and raise many questions, especially as to the interpretation of certain musical signs they contain. This article aims to open up new perspectives in the study of an enigmatic V-shaped melodic sign in the musical papyri of Menander (P.Oxy. LIII 3705).

Review on Sign Language Translation

Abstract: Sign language is a way of communicating using hand gestures, movements, and facial expressions, instead of spoken words. It is the medium of communication used by people who are deaf or have hearing impairments to exchange information between their own community and with normal people. In order to bridge the communication gap between people with hearing and speaking disabilities and people who do not use sign language, a lot of research work using machine learning algorithms has been done. Hence, Sign language translator came into picture. Sign Language Translators are generally used to interpret signs and gestures from deaf and hard hearing people and convert them into text.

Interpretasi Tanda Dalam Penokohan Cerpen “Syamsyūn Al- Jabbār” Karya Kāmil Kīlani (Kajian Semiotika Charles Sander Pierce)

This study discusses the types of signs and their interpretation characterizations in the short story Syamsyūn Al-Jabbār by Kāmil Kilanī. The purpose of study is to interpret of signs in the characterization of the short story Syamsyūn Al-Jabbār. The theory used in this research is pierce semiotics which is know as triangle sign theory, that is representament, object, and interpretant. Pierce also divides the types of signs based on his object into three, that is icons, indexs, and symbols. The method used in this study is a descriptive qualitative method that examines the contents of the document. Based on the result of research on the Syamsyūn Al-Jabbār short stories, it was found first, there are three types of signs icon, indexs, and symbols used to find out the characters contained in the Syamsyūn Al-Jabbār stories. The Characters included six figures including Syamsyūn Al-Jabbār, Jamilah, Sultan, Dalilah, Pandai besi, and Menteri. Second, the interpretation of sign. Overall in the short story there are three aspects related to life, including aspect of strength, power, and beauty.