{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:26:37Z","timestamp":1774401997895,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T00:00:00Z","timestamp":1671494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Agency for Technology and Standards","award":["K_G012002073401"],"award-info":[{"award-number":["K_G012002073401"]}]},{"name":"Korea Agency for Technology and Standards","award":["K_G012002234001"],"award-info":[{"award-number":["K_G012002234001"]}]},{"name":"Korea Agency for Technology and Standards","award":["K_G012002236201"],"award-info":[{"award-number":["K_G012002236201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the discipline of hand gesture and dynamic sign language recognition, deep learning approaches with high computational complexity and a wide range of parameters have been an extremely remarkable success. However, the implementation of sign language recognition applications for mobile phones with restricted storage and computing capacities is usually greatly constrained by those limited resources. In light of this situation, we suggest lightweight deep neural networks with advanced processing for real-time dynamic sign language recognition (DSLR). This paper presents a DSLR application to minimize the gap between hearing-impaired communities and regular society. The DSLR application was developed using two robust deep learning models, the GRU and the 1D CNN, combined with the MediaPipe framework. In this paper, the authors implement advanced processes to solve most of the DSLR problems, especially in real-time detection, e.g., differences in depth and location. The solution method consists of three main parts. First, the input dataset is preprocessed with our algorithm to standardize the number of frames. Then, the MediaPipe framework extracts hands and poses landmarks (features) to detect and locate them. Finally, the features of the models are passed after processing the unification of the depth and location of the body to recognize the DSL accurately. To accomplish this, the authors built a new American video-based sign dataset and named it DSL-46. DSL-46 contains 46 daily used signs that were presented with all the needed details and properties for recording the new dataset. The results of the experiments show that the presented solution method can recognize dynamic signs extremely fast and accurately, even in real-time detection. The DSLR reaches an accuracy of 98.8%, 99.84%, and 88.40% on the DSL-46, LSA64, and LIBRAS-BSL datasets, respectively.<\/jats:p>","DOI":"10.3390\/s23010002","type":"journal-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T03:29:20Z","timestamp":1671506960000},"page":"2","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Light-Weight Deep Learning Techniques with Advanced Processing for Real-Time Hand Gesture Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7351-625X","authenticated-orcid":false,"given":"Mohamed S.","family":"Abdallah","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea"},{"name":"Informatics Department, Electronics Research Institute (ERI), Cairo 11843, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6885-4315","authenticated-orcid":false,"given":"Gerges H.","family":"Samaan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan 11731, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4386-4135","authenticated-orcid":false,"given":"Abanoub R.","family":"Wadie","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan 11731, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-0137","authenticated-orcid":false,"given":"Fazliddin","family":"Makhmudov","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-7599","authenticated-orcid":false,"given":"Young-Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"ref_1","unstructured":"(2022, November 10). Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/deafness-and-hearing-loss."},{"key":"ref_2","first-page":"30","article-title":"Facial Expression Phoenix FePh An Annotated Sequenced Dataset for Facial and Emotion Specified Expressions in Sign Language","volume":"3","author":"Alaghband","year":"2021","journal-title":"Eng. World"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.imavis.2014.04.012","article-title":"Dynamic\u2013Static unsupervised sequentiality, statistical subunits and lexicon for sign language recognition","volume":"32","author":"Theodorakis","year":"2014","journal-title":"Image Vis. Comput."},{"key":"ref_4","first-page":"27","article-title":"Dynamic hand gesture recognition of arabic sign language using hand motion trajectory features","volume":"13","author":"Abdalla","year":"2013","journal-title":"Glob. J. Comput. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s13042-017-0705-5","article-title":"A review of hand gesture and sign language recognition techniques","volume":"10","author":"Cheok","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s11831-019-09384-2","article-title":"Sign Language Recognition Systems: A Decade Systematic Literature Review","volume":"28","author":"Wadhawan","year":"2012","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113794","DOI":"10.1016\/j.eswa.2020.113794","article-title":"Sign language recognition: A deep survey","volume":"164","author":"Rastgoo","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Escobedo, E., Ramirez, L., and Camara, G. (2019, January 28\u201330). Dynamic Sign Language Recognition Based on Convolutional Neural Networks and Texture Maps. Proceedings of the 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Rio de Janeiro, Brazil.","DOI":"10.1109\/SIBGRAPI.2019.00043"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"38044","DOI":"10.1109\/ACCESS.2019.2904749","article-title":"Dynamic sign language recognition based on video sequence with blstm-3d residual networks","volume":"7","author":"Liao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chaikaew, A., Somkuan, K., and Yuyen, T. (2021, January 3\u20136). Thai sign language recognition: An application of deep neural network. Proceedings of the 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, Cha-am, Thailand.","DOI":"10.1109\/ECTIDAMTNCON51128.2021.9425711"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10639","DOI":"10.1109\/JIOT.2019.2940368","article-title":"Recurrent Neural Networks for Accurate RSSI Indoor Localization","volume":"6","author":"Hoang","year":"2019","journal-title":"IEEE Int. Things J."},{"key":"ref_12","unstructured":"Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, Ch., and Grundmann, M. (2020). MediaPipe Hands: On-device Real-time Hand Tracking. arXiv."},{"key":"ref_13","unstructured":"Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., and Grundmann, M. (2020). BlazePose: On-device Real-time Body Pose tracking. arXiv."},{"key":"ref_14","unstructured":"De Giusti, L.C., Chichizola, F., Rodriguez Eguren, S., S\u00e1nchez, M., Paniego, J.M., and De Giusti, A.E. (2016, January 3\u20137). LSA64: An Argentinian sign language dataset. Proceedings of the XXII Congreso Argentino de Ciencias de la Computaci\u00f3n (CACIC 2016), San Luis, Argentina."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"114179","DOI":"10.1016\/j.eswa.2020.114179","article-title":"A multimodal LIBRAS-UFOP Brazilian sign language dataset of minimal pairs using a microsoft Kinect sensor","volume":"167","author":"Cerna","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_16","first-page":"1878","article-title":"Sign language recognition using leap motion controller","volume":"3","author":"Sonawane","year":"2017","journal-title":"Int. J. Adv. Res. Innov. Ideas Edu."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2850421","article-title":"Sign transition modeling and a scalable solution to continuous sign language recognition for real-world applications","volume":"8","author":"Li","year":"2016","journal-title":"ACM Trans. Access. Comput. (TACCESS)"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/JBHI.2016.2560907","article-title":"Chinese sign language recognition based on an optimized tree-structure framework","volume":"21","author":"Yang","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, T., Zhou, W., 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_20","doi-asserted-by":"crossref","unstructured":"Samaan, G.H., Wadie, A.R., Attia, A.K., Asaad, A.M., Kamel, A.E., Slim, S.O., Abdallah, M.S., and Cho, Y.-I. (2022). MediaPipe\u2019s Landmarks with RNN for Dynamic Sign Language Recognition. Electronics, 11.","DOI":"10.3390\/electronics11193228"},{"key":"ref_21","unstructured":"Cardenas, E.E., and Camara-Chavez, G. (2017). Fusion of Deep Learning Descriptors for Gesture Recognition Iberoamerican Congress on Pattern Recognition, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pigou, L., Dieleman, S., Kindermans, P.-J., and Schrauwen, B. (2014). Sign Language Recognition Using Convolutional Neural Networks Workshop at the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-16178-5_40"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Camgoz, N.C., Hadfield, S., Koller, O., and Bowden, R. (2016, January 4\u20138). Using convolutional 3d neural networks for user-independent continuous gesture recognition. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7899606"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"ElBadawy, M., Elons, A., Shedeed, H.A., and Tolba, M. (2017, January 5\u20137). Arabic sign language recognition with 3D convolutional neural networks Intelligent computing and information systems (ICICIS). Proceedings of the 2017 Eighth International Conference on Tools with Artificial Intelligence, IEEE, Boston, MA, USA.","DOI":"10.1109\/INTELCIS.2017.8260028"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pu, J., Zhou, W., and Li, H. (2018, January 13\u201319). Dilated convolutional network with iterative optimization for continuous sign language recognition. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/123"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rao, G.A., Syamala, K., Kishore, P., and Sastry, A. (2018, January 4\u20135). Deep convolutional neural networks for sign language recognition. Proceedings of the 2018 Conference on Signal Processing in Addition, Communication Engineering Systems (SPACES), Vijayawada, India.","DOI":"10.1109\/SPACES.2018.8316344"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cui, R., Liu, H., and Zhang, C. (2017, January 21\u201326). Recurrent convolutional neural networks for continuous sign language recognition by staged optimization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.175"},{"key":"ref_28","unstructured":"Gupta, P.M.X.Y.S., and Kautz, K.K.S.T.J. (June, January USA). Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3d Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV."},{"key":"ref_29","unstructured":"Montes y G\u00f3mez, M., Escalante, H., Segura, A., and Murillo, J. (2016). Sign Languague Recognition Without Frame-Sequencing Constraints: A Proof of Concept on the Argentinian Sign Language. Advances in Artificial Intelligence\u2014IBERAMIA 2016, Springer. Lecture Notes in Computer Science."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Campilho, A., Karray, F., and ter Haar Romeny, B. (2018). Sign Language Recognition Based on 3D Convolutional Neural Networks. Image Analysis and Recognition. ICIAR 2018, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-93000-8"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Gupta, S., Kim, K., and Pulli, K. (2015, January 4\u20138). Multi-sensor system for driver\u2019s hand-gesture recognition. Proceedings of the 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia.","DOI":"10.1109\/FG.2015.7163132"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Konstantinidis, D., Dimitropoulos, K., and Daras, P. (2018, January 3\u20135). Sign Language Recognition Based on Hand and Body Skeletal Data. Proceedings of the 2018-3DTV-Conference: The True Vision\u2014Capture, Transmission and Display of 3D Video (3DTV-CON), Helsinki, Finland.","DOI":"10.1109\/3DTV.2018.8478467"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Konstantinidis, D., Dimitropoulos, K., and Daras, P. (2018, January 16\u201318). A Deep Learning Approach for Analyzing Video and Skeletal Features in Sign Language Recognition. Proceedings of the 2018 IEEE International Conference on Imaging Systems and Techniques (IST), Krak\u00f3w, Poland.","DOI":"10.1109\/IST.2018.8577085"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wu, G., Li, Y., Yue, Y., and Zhou, X. (2021, January 7\u201310). Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System. Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New, Zealand.","DOI":"10.1109\/ICDM51629.2021.00108"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network","volume":"404","author":"Sherstinsky","year":"2018","journal-title":"Phys. Nonlinear Phenom."},{"key":"ref_36","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv."},{"key":"ref_37","unstructured":"Cahuantzi, R., Chen, X., and G\u00fcttel, S. (2021). A comparison of LSTM and GRU networks for learning symbolic sequences. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mateus, B.C., Mendes, M., Farinha, J.T., Assis, R., and Cardoso, A.M. (2021). Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press. Energies, 14.","DOI":"10.3390\/en14216958"},{"key":"ref_39","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An Introduction to Convolutional Neural Networks. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201324). Understanding of a convolutional neural network. Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mostavi, M., Chiu, Y.C., Huang, Y., and Chen, Y. (2020). Convolutional neural network models for cancer type prediction based on gene expression. BMC Med. Genom., 13.","DOI":"10.1186\/s12920-020-0677-2"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. 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