Pattern matching for automatic sign language translation system using LabVIEW

Domingo, Álvaro Arranz; Akmeliawati, Rini; Chow, Kuang Ye

doi:10.1109/icias.2007.4658470

Cited by 6 publications

(1 citation statement)

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“…Owing to the grammatical structure that needs interpretation stages, recognition and translation of long sentences (more than three words) have always been a challenge for sign languages. It is the common future work that researches expect to utilise their knowledge of grammatical checking and sentence structure into sign language system (Domingo et al, 2007).…”

Section: Background and Systematic Literature Reviewmentioning

confidence: 99%

Real-time sign languages character recognition

Awwad¹,

Idwan²,

Gharaibeh³

2021

IJCAT

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Deaf people face many challenges to communicate with the hearing world. Many studies and industrial solutions came up with interpretation from sign language to normal text but most of them are limited either to static images for letters or static animated character plays the word in motion. Therefore, this research worked on enhancing the feasible algorithms that have been used like image detection, image processing techniques and image translation methods. The contribution consists of two parts, the first one is using our data set, and the second one is proposing a new solution by extracting SURF features after using image filtering based on the existing methods to accelerate the translation process for the long sentences. Experimental results over our data set report improved accuracy compared with other studies in terms of efficiency time and recognition rate of sign language character recognition.

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Section: Background and Systematic Literature Reviewmentioning

confidence: 99%

Real-time sign languages character recognition

Awwad¹,

Idwan²,

Gharaibeh³

2021

IJCAT

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Computer Vision-Based Approach for Indian Sign Language Character Recognition Using CNN and ROI Segmentation

Selvan

Bagio

Rithesh

et al. 2021

Lecture Notes in Electrical Engineering

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Machine translation from signed to spoken languages: state of the art and challenges

Coster

Shterionov

Herreweghe

et al. 2023

Univ Access Inf Soc

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Automatic translation from signed to spoken languages is an interdisciplinary research domain on the intersection of computer vision, machine translation (MT), and linguistics. While the domain is growing in terms of popularity—the majority of scientific papers on sign language (SL) translation have been published in the past five years—research in this domain is performed mostly by computer scientists in isolation. This article presents an extensive and cross-domain overview of the work on SL translation. We first give a high level introduction to SL linguistics and MT to illustrate the requirements of automatic SL translation. Then, we present a systematic literature review of the state of the art in the domain. Finally, we outline important challenges for future research. We find that significant advances have been made on the shoulders of spoken language MT research. However, current approaches often lack linguistic motivation or are not adapted to the different characteristics of SLs. We explore challenges related to the representation of SL data, the collection of datasets and the evaluation of SL translation models. We advocate for interdisciplinary research and for grounding future research in linguistic analysis of SLs. Furthermore, the inclusion of deaf and hearing end users of SL translation applications in use case identification, data collection, and evaluation, is of utmost importance in the creation of useful SL translation models.

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Pattern matching for automatic sign language translation system using LabVIEW

Cited by 6 publications

References 0 publications

Real-time sign languages character recognition

Real-time sign languages character recognition

Computer Vision-Based Approach for Indian Sign Language Character Recognition Using CNN and ROI Segmentation

Machine translation from signed to spoken languages: state of the art and challenges

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