{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:23:13Z","timestamp":1784996593379,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Stiftelsen Dam"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>The application of machine learning models for sign language recognition (SLR) is a well-researched topic. However, many existing SLR systems focus on widely used sign languages, e.g., American Sign Language, leaving other underrepresented sign languages such as Norwegian Sign Language (NSL) relatively underexplored. This work presents a preliminary system for recognizing NSL gestures, focusing on numbers 0 to 10. Mediapipe is used for feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. This system achieves a testing accuracy of 95%, aligning with existing benchmarks and demonstrating its robustness to variations in signing styles, orientations, and speeds. While challenges such as data imbalance and misclassification of similar gestures (e.g., Signs 3 and 8) were observed, the results underscore the potential of our proposed approach. Future iterations of the system will prioritize expanding the dataset by including additional gestures and environmental variations as well as integrating additional modalities.<\/jats:p>","DOI":"10.3390\/mti9030023","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T05:52:16Z","timestamp":1740981136000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Real-Time Norwegian Sign Language Recognition Using MediaPipe and LSTM"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5215-1834","authenticated-orcid":false,"given":"Md. Zia","family":"Uddin","sequence":"first","affiliation":[{"name":"SINTEF Digital, 0373 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2741-8127","authenticated-orcid":false,"given":"Costas","family":"Boletsis","sequence":"additional","affiliation":[{"name":"SINTEF Digital, 0373 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P\u00e5l","family":"Rudshavn","sequence":"additional","affiliation":[{"name":"Statped, 7088 Heimdal, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2024, December 05). Deafness and Hearing Loss. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/deafness-and-hearing-loss."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Svendsen, B., and Kadry, S. (2023). Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition. Technologies, 11.","DOI":"10.3390\/technologies11040099"},{"key":"ref_3","unstructured":"World Federation of the Deaf (2024, December 05). Our Work. Available online: https:\/\/wfdeaf.org\/our-work\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pigou, L., Dieleman, S., Kindermans, P.J., and Schrauwen, B. (2014, January 6\u201312). Sign language recognition using convolutional neural networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-16178-5_40"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"25","DOI":"10.33166\/AETiC.2023.05.003","article-title":"Development of a Wearable Sensor Glove for Real-Time Sign Language Translation","volume":"7","author":"Ambar","year":"2023","journal-title":"Ann. Emerg. Technol. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/10400435.2016.1268218","article-title":"Ambient intelligence framework for real-time speech-to-sign translation","volume":"30","author":"Otoom","year":"2018","journal-title":"Assist. Technol."},{"key":"ref_7","unstructured":"Brega, J., Rodello, I., Dias, D.R.C., Salvador, V.F.M., and Guimar\u00e3es, M. (2014, January 10\u201313). A virtual reality environment to support chat rooms for hearing impaired and to teach Brazilian Sign Language (LIBRAS). Proceedings of the 2014 IEEE\/ACS 11th International Conference on Computer Systems and Applications (AICCSA), Doha, Qatar."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dewanto, F.M., Santoso, H.A., Shidik, G.F. (2024, January 21\u201322). Scoping Review Of Sign Language Recognition: An Analysis of MediaPipe Framework and Deep Learning Integration. Proceedings of the IEEE 2024 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia.","DOI":"10.1109\/iSemantic63362.2024.10761983"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.procs.2022.12.066","article-title":"American sign language recognition for alphabets using MediaPipe and LSTM","volume":"215","author":"Sundar","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhi, D., Oliveira, T.E.D., Prado da Fonseca, V., and Petriu, E. (2018, January 12\u201313). Teaching a Robot Sign Language using Vision-Based Hand Gesture Recognition. Proceedings of the 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Ottawa, ON, Canada.","DOI":"10.1109\/CIVEMSA.2018.8439952"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3592","DOI":"10.1109\/TIM.2011.2161140","article-title":"Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques","volume":"60","author":"Dardas","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alsharif, B., Alanazi, M., and Ilyas, M. (2023, January 4\u20136). Machine Learning Technology to Recognize American Sign Language Alphabet. Proceedings of the 2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life Using AI, Robotics and IoT (HONET), Boca Raton, FL, USA.","DOI":"10.1109\/HONET59747.2023.10374775"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guerrieri, B.T. (2024, January 20\u201323). Enhancing American Sign Language Classification by Leveraging Hand Landmark Extraction. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2. ACM, Portland, OR, USA.","DOI":"10.1145\/3626253.3635406"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101289","DOI":"10.1016\/j.pmcj.2020.101289","article-title":"DF-WiSLR: Device-Free Wi-Fi-based Sign Language Recognition","volume":"69","author":"Ahmed","year":"2020","journal-title":"Pervasive Mob. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ji, A., Wang, Y., Miao, X., Fan, T., Ru, B., Liu, L., Nie, R., and Qiu, S. (2023). Dataglove for Sign Language Recognition of People with Hearing and Speech Impairment via Wearable Inertial Sensors. Sensors, 23.","DOI":"10.3390\/s23156693"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Islam, M.S., Rahman, M.H., Sassi, R., Rivolta, M.W., and Aktaruzzaman, M. (2019, January 24\u201325). A New Benchmark on American Sign Language Recognition using Convolutional Neural Network. Proceedings of the 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh.","DOI":"10.1109\/STI47673.2019.9067974"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, Y., Xu, T., Han, S., and Kim, K. (2022). Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition. Appl. Sci., 12.","DOI":"10.3390\/app122211766"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"26319","DOI":"10.1007\/s11042-021-10768-5","article-title":"ASL-3DCNN: American Sign Language Recognition Technique Using 3-D Convolutional Neural Networks","volume":"80","author":"Sharma","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, X., Cao, C., and Duan, S. (2023). A Low-Power Hardware Architecture for Real-Time CNN Computing. Sensors, 23.","DOI":"10.3390\/s23042045"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Aydin, S., and Bilge, H.S. (2023, January 11\u201312). Optimal Hardware Implementation for End-to-End CNN-Based Classification. Proceedings of the 2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India.","DOI":"10.1109\/ICITIIT57246.2023.10068601"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, T., Zhou, W.g., and Li, H. (2016, January 25\u201328). Sign Language Recognition with Long Short-Term Memory. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532884"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Guo, D., Zhou, W.g., Li, H., and Wang, M. (2018, January 2\u20137). Hierarchical LSTM for Sign Language Translation. Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12235"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15911","DOI":"10.1109\/ACCESS.2022.3148132","article-title":"American Sign Language Words Recognition Using Spatio-Temporal Prosodic and Angle Features: A Sequential Learning Approach","volume":"10","author":"Abdullahi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_25","unstructured":"Mercanoglu, O., Tur, A.O., and Keles, H. (2019, January 24\u201326). Isolated Sign Language Recognition with Multi-scale Features using LSTM. Proceedings of the IEEE 2019 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, J., Chaijaruwanich, J., and Chouvatut, V. (March, January 28). Video-based Sign Language Recognition with R(2+1)D and LSTM Networks. Proceedings of the IEEE 2024 16th International Conference on Knowledge and Smart Technology (KST), Krabi, Thailand.","DOI":"10.1109\/KST61284.2024.10499646"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"58329","DOI":"10.1007\/s11042-023-17361-y","article-title":"Automatic Indian sign language recognition using MediaPipe holistic and LSTM network","volume":"83","author":"Khartheesvar","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rao, G., Sowmya, C., and Mamatha, D. (2023, January 25\u201328). Sign Language Recognition using LSTM and Media Pipe. Proceedings of the 2023 IEEE International Conference on Advances in Signal Processing and Communication Systems (SPCOM), Shanghai, China.","DOI":"10.1109\/ICICCS56967.2023.10142638"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"142","DOI":"10.22144\/ctujoisd.2023.045","article-title":"Exploring MediaPipe Optimization Strategies for Real-Time Sign Language Recognition","volume":"15","author":"Nguyen","year":"2023","journal-title":"CTU J. Innov. Sustain. Dev."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Farhan, Y., and Madi, A. (2022, January 1\u20132). Real-Time Dynamic Sign Recognition Using MediaPipe. Proceedings of the 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), Fez, Morocco.","DOI":"10.1109\/ICECOCS55148.2022.9982822"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s44163-024-00113-8","article-title":"Using LSTM to translate Thai sign language to text in real time","volume":"4","author":"Jintanachaiwat","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_32","unstructured":"Norges D\u00f8veforbund (2024, December 05). Norsk Tegnspr\u00e5k. Available online: https:\/\/www.doveforbundet.no\/tegnsprak\/."},{"key":"ref_33","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML-PMLR), Lille, France."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Irahal, F.N., Youssef, R.B., and Meyer, D. (2024, January 1\u20134). A Real-time Approach for Recognizing German Sign Language. Proceedings of the 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), Valetta, Malta.","DOI":"10.1109\/CoDIT62066.2024.10708392"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.35940\/ijitee.E9844.13050424","article-title":"Enhancing Arabic Sign Language Recognition using Deep Learning","volume":"13","author":"Sagheer","year":"2024","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104358","DOI":"10.1109\/ACCESS.2022.3210543","article-title":"Development of an End-to-End Deep Learning Framework for Sign Language Recognition, Translation, and Video Generation","volume":"10","author":"Natarajan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bird, J.J., Ek\u00e1rt, A., and Faria, D.R. (2020). British Sign Language Recognition via Late Fusion of Computer Vision and Leap Motion with Transfer Learning to American Sign Language. Sensors, 20.","DOI":"10.20944\/preprints202008.0209.v1"}],"container-title":["Multimodal Technologies and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2414-4088\/9\/3\/23\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:46:08Z","timestamp":1760028368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2414-4088\/9\/3\/23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,3]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["mti9030023"],"URL":"https:\/\/doi.org\/10.3390\/mti9030023","relation":{},"ISSN":["2414-4088"],"issn-type":[{"value":"2414-4088","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,3]]}}}