A Systematic Mapping of Translation-Enabling Technologies for Sign Languages

Naranjo-Zeledón, Luis; Peral, Jesús; Ferrández, Antonio; Chacón-Rivas, Mario

doi:10.3390/electronics8091047

Cited by 16 publications

(7 citation statements)

References 81 publications

(54 reference statements)

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“…Although various research sought to address the topic of sign language recognition in a systematic manner, nevertheless, their focus is not on the sign language recognition system based on vision. It is undeniable that there are few studies from Ardiansyah et al (2021);Naranjo et al (2019) that have done SLR on an issue related to sign language translation, but we have to consider that Ardiansyah et al (2021) study only touches on American Sign Language although there are many more sign languages in the world. Meanwhile, Naranjo et al (2019) do not focus on the visionbased method.…”

Section: Research Gap -The Existing Study Related To Vision-based Sig...mentioning

confidence: 99%

A Systematic Literature Review on Vision-Based Hand Gesture for Sign Language Translation

Johari¹,

Ramli²,

Zulkoffli³

et al. 2023

jkukm

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Deaf and hard of hearing people use sign language to communicate. People around mute and deaf people have difficulty communicating with each other if they do not understand sign language. This problem has prompted many researchers to conduct studies on sign language translation. However, there is a lack of compilation of SLR on this topic. Therefore, this paper aims to provide a thorough literature review of previous studies on sign language to text translation based on the vision method. PRISMA (Preferred Reporting Items to writing a standard Systematic Review and Meta-Analyses) is used in this systematic review. Two primary databases, Web of Science and Scopus, have been used to search for relevant articles and resources included in this systematic literature review. Based on the outcome of the systematic review of the topic, the primary studies on sign language translation systems were conducted using self-generated datasets more than public datasets. More static action sign language was studied compared to dynamic action sign language. For the type of recognition, more alphabet sign language was studied compared to digit, word, or sentence sign language. Other than that, most studies used digital cameras rather than Microsoft Kinect or a webcam. The most used classification method was Convolution Neural Network (CNN). The study is intended to guide readers and researchers for future research and knowledge enhancement in the field of sign language recognition.

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Section: Research Gap -The Existing Study Related To Vision-based Sig...mentioning

confidence: 99%

A Systematic Literature Review on Vision-Based Hand Gesture for Sign Language Translation

Johari¹,

Ramli²,

Zulkoffli³

et al. 2023

jkukm

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show abstract

“…The authors of Ref. [ 42 ] proposed a technology to map the remote sign language translation system so that the sign language translation support can be called at any time in the required scene. However, this method cannot solve the problem of two-way communication and it relies on the Internet, which would not provide real-time sign language interpretation support when the network fluctuates or there is no network service.…”

Section: Related Workmentioning

confidence: 99%

A Sign Language Recognition System Applied to Deaf-Mute Medical Consultation

Xia

Lü

Fan

et al. 2022

Sensors

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It is an objective reality that deaf-mute people have difficulty seeking medical treatment. Due to the lack of sign language interpreters, most hospitals in China currently do not have the ability to interpret sign language. Normal medical treatment is a luxury for deaf people. In this paper, we propose a sign language recognition system: Heart-Speaker. Heart-Speaker is applied to a deaf-mute consultation scenario. The system provides a low-cost solution for the difficult problem of treating deaf-mute patients. The doctor only needs to point the Heart-Speaker at the deaf patient and the system automatically captures the sign language movements and translates the sign language semantics. When a doctor issues a diagnosis or asks a patient a question, the system displays the corresponding sign language video and subtitles to meet the needs of two-way communication between doctors and patients. The system uses the MobileNet-YOLOv3 model to recognize sign language. It meets the needs of running on embedded terminals and provides favorable recognition accuracy. We performed experiments to verify the accuracy of the measurements. The experimental results show that the accuracy rate of Heart-Speaker in recognizing sign language can reach 90.77%.

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“…The research carried out in sign languages is presented in a systematic mapping study [3], making it clear that the recognition of these languages still presents many challenges, particularly in real-time software systems.…”

Section: Introductionmentioning

confidence: 99%

Phonological Proximity in Costa Rican Sign Language

et al. 2020

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The study of phonological proximity makes it possible to establish a basis for future decision-making in the treatment of sign languages. Knowing how close a set of signs are allows the interested party to decide more easily its study by clustering, as well as the teaching of the language to third parties based on similarities. In addition, it lays the foundation for strengthening disambiguation modules in automatic recognition systems. To the best of our knowledge, this is the first study of its kind for Costa Rican Sign Language (LESCO, for its Spanish acronym), and forms the basis for one of the modules of the already operational system of sign and speech editing called the International Platform for Sign Language Edition (PIELS). A database of 2665 signs, grouped into eight contexts, is used, and a comparison of similarity measures is made, using standard statistical formulas to measure their degree of correlation. This corpus will be especially useful in machine learning approaches. In this work, we have proposed an analysis of different similarity measures between signs in order to find out the phonological proximity between them. After analyzing the results obtained, we can conclude that LESCO is a sign language with high levels of phonological proximity, particularly in the orientation and location components, but they are noticeably lower in the form component. We have also concluded as an outstanding contribution of our research that automatic recognition systems can take as a basis for their first prototypes the contexts or sign domains that map to clusters with lower levels of similarity. As mentioned, the results obtained have multiple applications such as in the teaching area or the Natural Language Processing area for automatic recognition tasks.

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A Systematic Mapping of Translation-Enabling Technologies for Sign Languages

Cited by 16 publications

References 81 publications

A Systematic Literature Review on Vision-Based Hand Gesture for Sign Language Translation

A Systematic Literature Review on Vision-Based Hand Gesture for Sign Language Translation

A Sign Language Recognition System Applied to Deaf-Mute Medical Consultation

Phonological Proximity in Costa Rican Sign Language

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