{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T05:13:57Z","timestamp":1772169237469,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Between 2019 and 2022, as the Covid-19 pandemic unfolded, numerous countries implemented lockdown policies, leading most corporate companies to permit employees to work from home. Communication and meetings transitioned to online platforms, replacing face-to-face interactions. This shift posed challenges for deaf or hearing-impaired individuals who rely on sign language, using hand gestures for communication. However, it also affected those who can hear clearly but lack knowledge of sign language. Unfortunately, many online meeting platforms lack sign language translation features. This study addresses this issue, focusing on Thai sign language. The objective is to develop a model capable of translating Thai sign language in real-time. The Long Short-Term Memory (LSTM) architecture is employed in conjunction with MediaPipe Holistic for data collection. MediaPipe Holistic captures keypoints of hand, pose, and head, while the LSTM model translates hand gestures into a sequence of words. The model\u2019s efficiency is assessed based on accuracy, with real-time testing achieving an 86% accuracy, slightly lower than the performance on the test dataset. Nonetheless, there is room for improvement, such as expanding the dataset by collecting data from diverse individuals, employing data augmentation techniques, and incorporating an attention mechanism to enhance model accuracy.<\/jats:p>","DOI":"10.1007\/s44163-024-00113-8","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T13:03:02Z","timestamp":1709211782000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Using LSTM to translate Thai sign language to text in real time"],"prefix":"10.1007","volume":"4","author":[{"given":"Werapat","family":"Jintanachaiwat","sequence":"first","affiliation":[]},{"given":"Kritsana","family":"Jongsathitphaibul","sequence":"additional","affiliation":[]},{"given":"Nopparoek","family":"Pimsan","sequence":"additional","affiliation":[]},{"given":"Mintra","family":"Sojiphan","sequence":"additional","affiliation":[]},{"given":"Amorn","family":"Tayakee","sequence":"additional","affiliation":[]},{"given":"Traithep","family":"Junthep","sequence":"additional","affiliation":[]},{"given":"Thitirat","family":"Siriborvornratanakul","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"113_CR1","unstructured":"K. Manikandan, A. Patidar, P. Walia, AB. Roy, \u201cHand gesture detection and conversion to speech and text.\u201d International Conference on Innovations and Discoveries in Science, Engineering and Technology(ICIDSET), 2018."},{"issue":"5","key":"113_CR2","first-page":"1109","volume":"22","author":"H Wang","year":"2006","unstructured":"Wang H, Leu MC, Oz C. American sign language recognition using multi-dimensional hidden Markov models. J Inf Sci Eng. 2006;22(5):1109\u201323.","journal-title":"J Inf Sci Eng"},{"key":"113_CR3","doi-asserted-by":"crossref","unstructured":"Souza CR, Pizzolato EB. Sign language recognition with support vector machine and hidden conditional random fields: going from fingerspelling to natural articulated words. Machine learning and data mining in pattern recognition, lecture notes in computer science. 2013; 84\u201398.","DOI":"10.1007\/978-3-642-39712-7_7"},{"key":"113_CR4","unstructured":"Vedak O, Zavre P, Todkar A, Patil M. Sign language interpreter using image processing and machine learning. Int Res J Eng Technol (IRJET). 2019;6(4)."},{"issue":"5","key":"113_CR5","first-page":"2361","volume":"3","author":"M Tun","year":"2019","unstructured":"Tun M, Lwin T. Real-time Myanmar sign language recognition system using PCA and SVM. Int J Trends Sci Res Dev (IJTSRD). 2019;3(5):2361\u20136.","journal-title":"Int J Trends Sci Res Dev (IJTSRD)"},{"key":"113_CR6","unstructured":"Kumar PP, Reddy PVGDP, Rao PS. \u201cSIGN LANGUAGE RECOGNITION WITH MULTI FEATURE FUSION AND ADABOOST CLASSIFIER.\u201d ARPN J Eng Appl Sci, 13(4), 2018, pp. 1410-1419."},{"key":"113_CR7","doi-asserted-by":"crossref","unstructured":"Abiyev RH, Arslan M, Idoko JB. \u201cSign Language Translation Using Deep Convolutional Neural Networks.\u201d KSII Transactions on Internet and Information Systems (TIIS), 14(2), 2020; pp. 631-653.","DOI":"10.3837\/tiis.2020.02.009"},{"key":"113_CR8","unstructured":"Garcia B, Viesca SA. \u201cReal-time American Sign Language recognition with Convolutional Neural Networks.\u201d CS231n: convolutional Neural Networks for Visual Recognition, Stanford University - Spring 2016 student report, 2016; http:\/\/cs231n.stanford.edu\/reports\/2016\/pdfs\/214_Report.pdf"},{"key":"113_CR9","unstructured":"Elhagry A, Elrayes R. \u201cEgyptian sign language recognition using CNN and LSTM.\u201d arXiv, 2021; https:\/\/arxiv.org\/abs\/2107.13647."},{"key":"113_CR10","doi-asserted-by":"crossref","unstructured":"Guo D, Zhou W, Li H, Wang M. \u201cHierarchical LSTM for Sign Language Translation.\u201d Proceedings of the International AAAI Conference on Web and Social Media, 32(1), 2018;","DOI":"10.1609\/aaai.v32i1.12235"},{"key":"113_CR11","unstructured":"google\/mediapipe, \u201cGithub-google\/mediapipe\u201d, https:\/\/github.com\/google\/mediapipe."},{"key":"113_CR12","unstructured":"Lugaresi C, Tang J, Nash H, McClanahan C, Uboweja E, Hays M, Zhang F, Chang C, Yong MG, Lee J, Chang W, Hua W, Georg M, Grundmann M. \u201cMediaPipe: a framework for building perception pipelines.\u201d arXiv, 2019; https:\/\/arxiv.org\/abs\/1906.08172."},{"key":"113_CR13","unstructured":"Domenech A. \u201cSIGN LANGUAGE RECOGNITION: ASL Recognition with MediaPipe and Recurrent Neural Networks.\u201d Bachelor-Thesis, FH Aachen University of Applied Sciences, 2020; https:\/\/upcommons.upc.edu\/bitstream\/handle\/2117\/343984\/ASL"},{"key":"113_CR14","unstructured":"Vasilev I, Slater D, Spacagna G, Roelants P, Zocca V. \u201cPython deep learning: exploring deep learning techniques and neural network architectures with Pytorch, Keras, and TensorFlow.\u201d Packt Publishing Ltd, 2019;"},{"key":"113_CR15","doi-asserted-by":"crossref","unstructured":"Graves A, Jaitly N, Mohamed A. \u201cHybrid speech recognition with deep bidirectional LSTM.\u201d IEEE workshop on automatic speech recognition and understanding, 2013; pp. 273-278.","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"113_CR16","unstructured":"Colah blog, \u201cUnderstanding LSTM Networks.\u201d Posted on August 27, 2015, https:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/."},{"key":"113_CR17","unstructured":"Department of Empowerment of Persons with Disabilities, \u201cThe situation of people with disabilities on September 230, 2023.\u201d Posted on November 9, 2023., https:\/\/dep.go.th\/th\/law-academic\/knowledge-base\/disabled-person-situation\/"},{"key":"113_CR18","first-page":"170","volume":"4","author":"R Sreemathy","year":"2023","unstructured":"Sreemathy R, Turuk MP, Chaudhary S, Lavate K, Ushire A, Khurana S. Continuous word level sign language recognition using an expert system based on machine learning. 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Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal. ACM Transactions on Multimedia Computing: Communications, and Applications; 2024.","DOI":"10.1145\/3640815"},{"key":"113_CR25","doi-asserted-by":"crossref","unstructured":"Liu Y, Nand P, Hossain MA, Nguyen M, Yan WQ. \u201cSign language recognition from digital videos using feature pyramid network with detection transformer.\u201d Multimedia Tools and Applications, 82, pp. 21673-21685, 2023;","DOI":"10.1007\/s11042-023-14646-0"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00113-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-024-00113-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-024-00113-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T13:03:26Z","timestamp":1709211806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-024-00113-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,29]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["113"],"URL":"https:\/\/doi.org\/10.1007\/s44163-024-00113-8","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,29]]},"assertion":[{"value":"26 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not available as the results presented by this research do not involve any identifiable personal data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17"}}