{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:29:20Z","timestamp":1772206160481,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,27]],"date-time":"2025-04-27T00:00:00Z","timestamp":1745712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012024","name":"Multimedia University, Cyberjaya, Selangor, Malaysia","doi-asserted-by":"publisher","award":["MMUI\/240029"],"award-info":[{"award-number":["MMUI\/240029"]}],"id":[{"id":"10.13039\/100012024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Communication through sign language effectively helps both hearing- and speaking-impaired individuals connect. However, there are problems with the interlingual communication between Bangla Sign Language (BdSL) and English Sign Language (ASL) due to the absence of a unified system. This study aims to introduce a detection system that incorporates these two sign languages to enhance the flow of communication for those who use these forms of sign language. This study developed and tested a deep learning-based sign-language detection system that can recognize both BdSL and ASL alphabets concurrently in real time. The approach uses a YOLOv11 object detection architecture that has been trained with an open-source dataset on a set of 9556 images containing 64 different letter signs from both languages. Data preprocessing was applied to enhance the performance of the model. Evaluation criteria, including the precision, recall, mAP, and other parameter values were also computed to evaluate the model. The performance analysis of the proposed method shows a precision of 99.12% and average recall rates of 99.63% in 30 epochs. The studies show that the proposed model outperforms the current techniques in sign language recognition (SLR) and can be used in communicating assistive technologies and human\u2013computer interaction systems.<\/jats:p>","DOI":"10.3390\/jimaging11050134","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T04:25:50Z","timestamp":1745814350000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9238-1414","authenticated-orcid":false,"given":"Nawshin","family":"Navin","sequence":"first","affiliation":[{"name":"Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2625-2348","authenticated-orcid":false,"given":"Fahmid Al","family":"Farid","sequence":"additional","affiliation":[{"name":"Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, Selangor, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0072-5930","authenticated-orcid":false,"given":"Raiyen Z.","family":"Rakin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh"}]},{"given":"Sadman S.","family":"Tanzim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh"}]},{"given":"Mashrur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6375-4174","authenticated-orcid":false,"given":"Shakila","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3403-4095","authenticated-orcid":false,"given":"Jia","family":"Uddin","sequence":"additional","affiliation":[{"name":"AI and Big Data Department, Woosong University, Daejeon 34606, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7613-4596","authenticated-orcid":false,"given":"Hezerul Abdul","family":"Karim","sequence":"additional","affiliation":[{"name":"Centre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, Selangor, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,27]]},"reference":[{"key":"ref_1","first-page":"1021","article-title":"Review of Sign Language Recognition Based on Deep Learning","volume":"42","author":"Zhang","year":"2020","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, X., Jettanasen, C., and Chiradeja, P. (2025). Exploration of Sign Language Recognition Methods Based on Improved YOLOv5s. Computation, 13.","DOI":"10.3390\/computation13030059"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., and Ray, K. (2020). Proceeding of International Conference on Trends in Computational and Cognitive Engineering, Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-981-33-4673-4"},{"key":"ref_4","first-page":"1534","article-title":"Global, regional, and national incidence, prevalence, and years lived with disabilities for 310 diseases and injuries, 1900\u20132015: A systematic analysis for the Global Burden of Disease Study 2015","volume":"388","author":"Vos","year":"2016","journal-title":"Lacet"},{"key":"ref_5","unstructured":"WHO (2024). Deafness and Hearing Loss, World Health Organization. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/deafness-and-hearing-loss."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1080\/13682199.2020.1724438","article-title":"Arabic Sign Language Intelligent Translator","volume":"68","author":"Ahmed","year":"2020","journal-title":"Imaging Sci. J."},{"key":"ref_7","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, NeurIPS."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 26\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Q., Sun, G., Gu, L., and Liu, Z. (2021, January 3\u20135). Object Detection of Surgical Instruments Based on YOLOv4. Proceedings of the 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM), Chongqing, China.","DOI":"10.1109\/ICARM52023.2021.9536075"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105198","DOI":"10.1016\/j.engappai.2022.105198","article-title":"A comprehensive survey and taxonomy of sign language research","volume":"114","author":"Luqman","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, Y., Cheng, R., Zhang, C., Chen, M., Ma, J., and Shi, X. (2022, January 28\u201330). Sign language letters recognition model based on improved YOLOv5. Proceedings of the 2022 9th International Conference on Digital Home (ICDH), Guangzhou, China.","DOI":"10.1109\/ICDH57206.2022.00036"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"123138","DOI":"10.1109\/ACCESS.2019.2938829","article-title":"User-Independent American Sign Language Alphabet Recognition Based on Depth Image and PCANet Features","volume":"7","author":"Aly","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2735952","article-title":"A real-time hand posture recognition system using Deep Neural Networks","volume":"6","author":"Tang","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_15","first-page":"1186","article-title":"Real-Time Static and Dynamic Sign Language Recognition Using Deep Learning","volume":"81","author":"Jayanthi","year":"2022","journal-title":"J. Sci. Ind. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jiang, H., Sun, Y., and Xu, L. (2024). A Static Sign Language Recognition Method Enhanced with Self-Attention Mechanisms. Sensors, 24.","DOI":"10.3390\/s24216921"},{"key":"ref_17","first-page":"99","article-title":"Comparative Analysis on Real-Time Hand Gesture and Sign Language Recognition Using Convexity Defects and YOLOv3","volume":"42","author":"Khaliluzzaman","year":"2024","journal-title":"Sigma J. Eng. Nat. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5339","DOI":"10.1007\/s00521-020-05337-0","article-title":"Convolutional neural network with spatial pyramid pooling for hand gesture recognition","volume":"33","author":"Tan","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_19","first-page":"10","article-title":"Sign Language Classifier based on Machine Learning","volume":"123","author":"Avram","year":"2024","journal-title":"Technol. I Autom. Monta\u017cu Assem. Tech. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"21673","DOI":"10.1007\/s11042-023-14646-0","article-title":"Sign Language Recognition from Digital Videos Using Feature Pyramid Network with Detection Transformer","volume":"82","author":"Liu","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shin, J., Matsuoka, A., Hasan, M.A.M., and Srizon, A.Y. (2021). American Sign Language Alphabet Recognition by Extracting Features from Hand Pose Estimation. Sensors, 21.","DOI":"10.3390\/s21175856"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"114403","DOI":"10.1016\/j.eswa.2020.114403","article-title":"American Sign Language Recognition and Training Method with Recurrent Neural Network","volume":"167","author":"Lee","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_23","unstructured":"Mariappan, H.M., and Gomathi, V. (2019, January 21\u201323). Real-Time Recognition of Indian Sign Language. Proceedings of the 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India."},{"key":"ref_24","first-page":"695","article-title":"An Integrated CNN-LSTM Model for Bangla Lexical Sign Language Recognition","volume":"57","author":"Basnin","year":"2020","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_25","first-page":"200224","article-title":"Deep Learning-Based Bangla Sign Language Detection with an Edge Device","volume":"18","author":"Siddique","year":"2023","journal-title":"Intell. Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"118914","DOI":"10.1016\/j.eswa.2022.118914","article-title":"A Hybrid Approach for Bangla Sign Language Recognition Using Deep Transfer Learning Model with Random Forest Classifier","volume":"213","author":"Das","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.procs.2018.10.438","article-title":"A Potent Model to Recognize Bangla Sign Language Digits Using Convolutional Neural Network","volume":"143","author":"Islam","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3410837","DOI":"10.1109\/ACCESS.2024.3410837","article-title":"MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition","volume":"12","author":"Shams","year":"2024","journal-title":"IEEE Access"},{"key":"ref_29","unstructured":"Islam, M.S., Joha, A.J.M.A., Hossain, M.N., Abdullah, S., Elwarfalli, I., and Hasan, M.M. (2023). Word-Level Bangla Sign Language Dataset for Continuous BSL Recognition. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Islam, M.A., Karim, A., Rahaman, M.A., Rahman, M., Hossain, M.P., and Kabir, S.A. (2022, January 17\u201318). Automatic 3D Animated Bangla Sign Language Gestures Generation from Bangla Text and Voice. Proceedings of the 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh.","DOI":"10.1109\/STI56238.2022.10103252"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rahaman, M.A., Jasim, M., Ali, M.H., and Hasanuazzaman, M. (2020). Bangla Language Modeling Algorithm for Automatic Recognition of Hand-Sign-Spelled Bangla Sign Language. Front. Comput. Sci., 14.","DOI":"10.1007\/s11704-018-7253-3"},{"key":"ref_32","first-page":"109799","article-title":"BdSL47: A Complete Depth-Based Bangla Sign Alphabet and Digit Dataset","volume":"V1","author":"Mahmud","year":"2023","journal-title":"Mendeley Data"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_34","unstructured":"Perez, L., and Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification using Deep Learning. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"987","DOI":"10.11591\/ijaas.v13.i4.pp987-999","article-title":"Real-Time Smoke and Fire Detection Using You Only Look Once v8-Based Advanced Computer Vision and Deep Learning","volume":"13","author":"Shakila","year":"2024","journal-title":"Int. J. Adv. Appl. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9269","DOI":"10.1007\/s10489-024-05588-7","article-title":"A real-time human bone fracture detection and classification from multi-modal images using deep learning technique","volume":"54","author":"Parvin","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.3390\/vehicles6030065","article-title":"Comparative Analysis of YOLOv8 and YOLOv10 in Vehicle Detection: Performance Metrics and Model Efficacy","volume":"6","author":"Geetha","year":"2024","journal-title":"Vehicles"},{"key":"ref_38","first-page":"3433","article-title":"A Deep Learning Model for YOLOv9-Based Human Abnormal Activity Detection: Violence and Non-Violence Classification","volume":"20","author":"Sirajus","year":"2024","journal-title":"Int. J. Electr. Electron. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rahman, S., Rony, J.H., Uddin, J., and Samad, M.A. (2023). Real-Time Obstacle Detection with YOLOv8 in a WSN Using UAV Aerial Photography. J. Imaging, 9.","DOI":"10.3390\/jimaging9100216"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/5\/134\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:22:51Z","timestamp":1760030571000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/5\/134"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,27]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["jimaging11050134"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11050134","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,27]]}}}