AI-Based Approaches for Brazilian Sign Language Recognition: A Systematic Literature Review: Insights on Methods, Metrics, and Resources for LIBRAS Recognition
Resumo
This Systematic Literature Review (SLR) investigates the application of Artificial Intelligence (AI), specifically with Computer Vision (CV) and Natural Language Processing (NLP) techniques for Brazilian Sign Language (LIBRAS) recognition, translation, and interpretation. Following the PRISMA protocol and using the Parsifal tool to support protocol definition and study management, 77 studies were initially identified. After removing duplicates and applying exclusion criteria, 23 studies were selected for in-depth analysis. The review addresses three research questions: (i) AI-based solutions currently employed for LIBRAS recognition, (ii) reported performance metrics and evaluation results, and (iii) datasets used for training and testing, along with their characteristics and limitations. The findings highlight advances in deep learning architectures and computer vision pipelines, while revealing persistent challenges such as limited integration of non-manual features, scarcity of large-scale and multimodal datasets, difficulties in continuous sign recognition, heterogeneous annotation practices, and inadequacy of common bilingual evaluation metrics for capturing syntactic and semantic differences between LIBRAS and Portuguese. Moreover, deployment-related factors, including inference time and computational cost, remain underexplored. These insights provide guidance for future research toward more accurate, efficient, and inclusive LIBRAS recognition systems.
Palavras-chave:
Sign Language Recognition, Brazilian Sign Language, LIBRAS, Artificial Intelligence, Neural Networks, Literature Review
Referências
Bashaer Abdullah, Ghada Amoudi, and Hanan Alghamdi. 2024. Advancements in Sign Language Recognition: A Comprehensive Review and Future Prospects. IEEE Access PP (01 2024), 1–1. DOI: 10.1109/ACCESS.2024.3457692
Filipe Abreu and Jorge Dias. 2018. A Dataset for Electromyography-Based Dactylology Recognition. In Proceedings of the 9th International Conference on Advances in Computer-Human Interactions (ACHI 2016). IARIA, Lisbon, Portugal, 325–330. [link]
João Gabriel Abreu, João Marcelo Teixeira, Lucas Silva Figueiredo, and Veronica Teichrieb. 2016. Evaluating Sign Language Recognition Using the Myo Armband. (2016).
Carlos Eduardo G. R. Alves, Francisco De A. Boldt, and Thiago M. Paixão. 2024. Enhancing Brazilian Sign Language Recognition Through Skeleton Image Representation. (2024).
Tejaswini Ananthanarayana, Priyanshu Srivastava, Akash Chintha, Akhil Santha, Brian Landy, Joseph Panaro, Andre Webster, Nikunj Kotecha, Shagan Sah, Thomastine Sarchet, Raymond Ptucha, and Ifeoma Nwogu. 2021. Deep Learning Methods for Sign Language Translation. ACM Trans. Access. Comput. 14, 4, Article 22 (Oct. 2021), 30 pages. DOI: 10.1145/3477498
Tadas Baltrusaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency. 2018. OpenFace 2.0: Facial Behavior Analysis Toolkit. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). 59–66. DOI: 10.1109/FG.2018.00019
Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65–72.
Igor L.O. Bastos, Michele F. Angelo, and Angelo C. Loula. 2015. Recognition of Static Gestures Applied to Brazilian Sign Language (Libras). (2015).
S. Bharti, A. Balmik, and A. Nandy. 2023. Novel error correction-based key frame extraction technique for dynamic hand gesture recognition. Neural Computing and Applications (2023).
Danielle Bragg, Oscar Koller, Mary Bellard, Larwan Berke, Patrick Boudreault, Annelies Braffort, Naomi Caselli, Matt Huenerfauth, Hernisa Kacorri, Tessa Verhoef, Christian Vogler, and Meredith Ringel Morris. 2019. Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective. In Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility (Pittsburgh, PA, USA) (ASSETS ’19). Association for Computing Machinery, New York, NY, USA, 16–31. DOI: 10.1145/3308561.3353774
Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2021. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 1 (2021), 172–186. DOI: 10.1109/TPAMI.2019.2929257
Fabio Cappabianco, Igor Ribeiro Ferreira de Matos, and Luiz Eduardo Galvão Martins. 2024. A Systematic Literature Review on Computer Vision Applied to Brazilian Sign Language. Luiz Eduardo Galvão, A Systematic Literature Review on Computer Vision Applied to Brazilian Sign Language (2024).
João Carreira and Andrew Zisserman. 2017. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4724–4733. DOI: 10.1109/CVPR.2017.502
Omdena São Paulo Chapter. 2023. Brazilian Sign Language Datasets: UFV, INES, SignBank, V-Libras. [link] BrazilianSignLanguage. Acesso em: jul. 2025.
D.R.B. da Silva, T.M.U. de Araújo, T.G. do Rêgo, L.C.S. Florêncio, A.F. Neto, and A.C. de Paiva. 2023. A multiple stream architecture for the recognition of signs in Brazilian sign language in the context of health. Multimedia Tools and Applications (2023).
Diego R. B. da Silva, Tiago Maritan U. Araujo, Thais Gaudencio do Rêgo, and Manuella Aschoff Cavalcanti Brandão. 2020. A Two-Stream Model Based on 3D Convolutional Neural Networks for the Recognition of Brazilian Sign Language in the Health Context. (2020).
Amanda Hellen de Avellar Sarmento and Moacir Antonelli Ponti. 2023. A Cross-Dataset Study on the Brazilian Sign Language Translation. (2023).
Aron Caiuá V. De Brito, Ana Patrícia F. M. Mascarenhas, Marco A.C. Simões, Ivanoé J. Rodowanski, Jorge Alberto P. De Campos, Josemar Rodrigues De Souza, and Jose Grimaldo Da Silva Filho. 2024. RoboSign: Improving Interaction Between Service-Robot and Hearing Impairments People. (2024).
Ronice Müller de Quadros. 2006. Gramática da Língua de Sinais Brasileira. Editora Bookman, Porto Alegre, Brasil. Obra de referência sobre a estrutura linguística da LIBRAS.
Universidade Federal de Santa Catarina (UFSC). 2020. Corpus Libras UFSC. [link]. Acesso em: jul. 2025.
Giely M. C. de Souza. 2019. Tradução Automática Português-Libras: Uma abordagem híbrida baseada em RBMT e NMT. Ph.D. Dissertation. Universidade Federal de Santa Catarina. [link]
Universidade Federal de Viçosa. 2020. Dicionário Online Bilíngue Libras/ Português. [link]. Acesso em: 11 ago. 2025.
Mariza Miola Dosciatti, Lohann Paterno Coutinho Ferreira, and Emerson Cabrera Paraiso. 2013. Identificando Emoções em Textos em Português do Brasil usando Máquina de Vetores de Suporte em Solução Multiclasse. In Congresso Nacional de Iniciação Científica - CONIC. Brasil. [link] Disponível em: [link].
Rodrigo Zempulski Fanucchi, Arlindo Rodrigues Galvão Junior, Gabriel da Mata Marques, Lucas Brandão Rodrigues, Anderson da Silva Soares, and Telma Woerle Lima Soares. 2024. Fine-Tuning a Video Masked Autoencoder to Develop an Augmented Reality Application for Brazilian Sign Language Interpretation. (2024).
Andrew Flores Brongar, Jordan Zitzke Pinho, Rosa Vicari, Gisele Moraes Simas, and Regina Barwaldt. 2024. Improving Translation from Portuguese to Brazilian Sign Language with Speech-to-Text Integration Through Natural Language Processing. (2024).
Silas Luiz Furtado, Jauvane C. De Oliveira, and Shervin Shirmohammadi. 2023. Interactive and Markerless Visual Recognition of Brazilian Sign Language Alphabet. (2023).
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770–778. DOI: 10.1109/CVPR.2016.90
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735
Glenn Jocher. 2020. YOLOv5 by Ultralytics. DOI: 10.5281/zenodo.3908559
Daniel Jurafsky and James H. Martin. 2019. Speech and Language Processing (3 ed.). Pearson. Draft available at [link].
H Pallab Jyoti Dutta, Debajit Sarma, M.K. Bhuyan, and R. H. Laskar. 2020. Semantic Segmentation based Hand Gesture Recognition using Deep Neural Networks. (2020).
Navroz Kaur Kahlon and Williamjeet Singh. 2023. Machine translation from text to sign language: a systematic review. Universal Access in the Information Society 22, 1 (2023), 1–35.
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2016. Autoencoding beyond pixels using a learned similarity metric. In International conference on machine learning. PMLR, 1558–1566.
Chin-Yew Lin. 2004. ROUGE: A Package for Automatic Evaluation of Summaries. In Text Summarization Branches Out: Proceedings of the ACL-04 Workshop. Association for Computational Linguistics.
Matthew J. Page, Joanne E. McKenzie, Patrick M. Bossuyt, Isabelle Boutron, Tammy C. Hoffmann, Cynthia D. Mulrow, Larissa Shamseer, Jennifer M. Tetzlaff, Elie A. Akl, Sue E. Brennan, Roger Chou, Julie Glanville, Jeremy M. Grimshaw, Asbjørn Hróbjartsson, Manoj M. Lalu, Tianjing Li, ElizabethW. Loder, Evan Mayo-Wilson, Steve McDonald, Luke A. McGuinness, Lesley A. Stewart, James Thomas, Andrea C. Tricco, and Vivian A. Welch. 2021. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372 (2021), n71. DOI: 10.1136/bmj.n71
Papers with Code. 2025. Inception-v3. [link] Licença: CC-BY-SA.
Parsifal. n.d.. About Parsifal: Perform Systematic Literature Reviews. [link]. Accessed: 2025-08-11.
Michele D. Peters, Christina M. Godfrey, Deborah McInerney, Patricia Soares, Hanan Khalil, and Beverley J. Parker. 2015. The Joanna Briggs Institute Reviewers’ Manual: Methodology for JBI Scoping Reviews. The Joanna Briggs Institute (2015). Available online: [link].
Presidência da República do Brasil. 2002. Lei nº 10.436, de 24 de abril de 2002. [link]. Recognizes the Brazilian Sign Language (Libras) as a legal means of communication and expression..
Presidência da República do Brasil. 2005. Decreto nº 5.626, de 22 de dezembro de 2005. [link]. Regulates Lei nº 10.436/2002 and mandates the provision of accessibility for deaf people in education, media, and public services..
Soraia Prietch, J. Alfredo Sánchez, and Josefina Guerrero. 2022. A Systematic Review of User Studies as a Basis for the Design of Systems for Automatic Sign Language Processing. ACM Trans. Access. Comput. 15, 4, Article 36 (Nov. 2022), 33 pages. DOI: 10.1145/3563395
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 1, Article 140 (Jan. 2020), 67 pages.
UCI Machine Learning Repository. 2008. Libras Movement Dataset. [link]. 15 classes de movimentos manuais. Acesso em: jul. 2025.
Tamires et al. Rezende. 2022. MINDS-Libras:UmConjunto Multimodal para Sinais em Libras. [link]. Acesso mediante solicitação. UFMG. Acesso em: jul. 2025.
Jampierre Rocha, Jeniffer Lensk, Taís Ferreira, and Marcelo Ferreira. 2020. Towards a Tool to Translate Brazilian Sign Language (Libras) to Brazilian Portuguese and Improve Communication with Deaf. (2020).
Ailton José Rodrigues and Emmanuel P. Barbosa. 2020. Corpus paralelo Português-Libras para tradução automática baseado em avatares 3D. [link]. Acesso em: jul. 2025.
Karl Rohr. 2001. Landmark-based image analysis: using geometric and intensity models. Vol. 21. Springer Science & Business Media.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 234–241.
Daniel Sánchez Ruiz, J. Arturo Olvera-López, and Ivan Olmos-Pineda. 2023. Word Level Sign Language Recognition via Handcrafted Features. IEEE Latin America Transactions (2023).
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4510–4520. DOI: 10.1109/CVPR.2018.00474
Debajit Sarma, Hridoy Jyoti Prasad Dutta, Krishna Singh Yadav, Sahin Ahmed, and Manash Hazarika. 2021. Attention-based hand semantic segmentation and gesture recognition using deep networks. Evolving Systems (2021).
Saurav Kumar Sarma, Hemanta Kalita, and Rumi Devi. 2024. Semantic Segmentation based Hand Gesture Recognition using Deep Neural Networks. In Proceedings of the 2nd International Conference on Computer Vision and Robotics (CVR 2024). Springer, 102–109. DOI: 10.1007/978-981-99-9053-5_9
Marcos Silva, Tiago Araújo, Rostand Costa, Samuel Moreira, and Lucas Alves. 2021. Automation: Uma Ferramenta para a Evolução de um Tradutor Automático Português-Libras com Corpus Colaborativo. (2021).
José Silveira, Rodrigo Vieira, and Adriano Bastos. 2023. SynLibras-Pose Dataset. DOI: 10.5281/zenodo.8415850 Acesso em: jul. 2025.
Wellington Silveira, Andrew Alaniz, Marina Hurtado, Bernardo Castello Da Silva, and Rodrigo De Bem. 2022. SynLibras: A Disentangled Deep Generative Model for Brazilian Sign Language Synthesis. (2022).
Wellington Silveira, Andrew Alaniz, Marina Hurtado, Luca Mendonça, and Rodrigo de Bem. 2023. Towards Generating Digital LIBRAS Signers on Mobile Devices. (2023).
Karen Simonyan and AndrewZisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
Marina Sokolova and Guy Lapalme. 2009. A Systematic Analysis of Performance Measures for Classification Tasks. Information Processing & Management 45, 4 (2009), 427–437. DOI: 10.1016/j.ipm.2009.03.002
Esther de Oliveira Souza, Juliana Guimarães Faria, Layane Rodrigues de Lima, and Inés Martins. 2023. Digital Content in Brazilian Sign Language (Libras): A Study of Brazilian Channels of Deaf People on YouTube. Comunicação e sociedade 43 (2023), e023001.
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, Vol. 31.
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. 2022. VideoMAE: masked autoencoders are data-efficient learners for self-supervised video pre-training. In Proceedings of the 36th International Conference on Neural Information Processing Systems (New Orleans, LA, USA) (NIPS ’22). Curran Associates Inc., Red Hook, NY, USA, Article 732, 16 pages.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
Vinícius Veríssimo and Rostand Costa. 2020. Using Data Augmentation and Neural Networks to Improve the Emotion Analysis of Brazilian Portuguese Texts. (2020).
Vinícius Veríssimo, Cecília Silva, Vitor Hanael, Caio Moraes, Rostand Costa, Tiago Maritan, Manuella Aschoff, and Thaís Gaudêncio. 2019. A study on the use of sequence-to-sequence neural networks for automatic translation of brazilian portuguese to LIBRAS. (2019).
Equipe VLibras. 2020. VLibrasBD - Corpus paralelo para tradução automática Português–Libras. Disponível mediante solicitação institucional ao projeto VLibras..
Felix Wu, Angela Fan, Alexei Baevski, Yann N Dauphin, and Michael Auli. 2019. Pay less attention with lightweight and dynamic convolutions. arXiv preprint arXiv:1901.10430 (2019).
Hanaa ZainEldin, Samah A Gamel, Fatma M Talaat, Mansourah Aljohani, Nadiah A Baghdadi, Amer Malki, Mahmoud Badawy, and Mostafa A Elhosseini. 2024. Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication. Artificial Intelligence Review 57, 7 (2024), 188.
Yanqiong Zhang and Xianwei Jiang. 2023. Recent Advances on Deep Learning for Sign Language Recognition. CMES - Computer Modeling in Engineering and Sciences (2023).
Filipe Abreu and Jorge Dias. 2018. A Dataset for Electromyography-Based Dactylology Recognition. In Proceedings of the 9th International Conference on Advances in Computer-Human Interactions (ACHI 2016). IARIA, Lisbon, Portugal, 325–330. [link]
João Gabriel Abreu, João Marcelo Teixeira, Lucas Silva Figueiredo, and Veronica Teichrieb. 2016. Evaluating Sign Language Recognition Using the Myo Armband. (2016).
Carlos Eduardo G. R. Alves, Francisco De A. Boldt, and Thiago M. Paixão. 2024. Enhancing Brazilian Sign Language Recognition Through Skeleton Image Representation. (2024).
Tejaswini Ananthanarayana, Priyanshu Srivastava, Akash Chintha, Akhil Santha, Brian Landy, Joseph Panaro, Andre Webster, Nikunj Kotecha, Shagan Sah, Thomastine Sarchet, Raymond Ptucha, and Ifeoma Nwogu. 2021. Deep Learning Methods for Sign Language Translation. ACM Trans. Access. Comput. 14, 4, Article 22 (Oct. 2021), 30 pages. DOI: 10.1145/3477498
Tadas Baltrusaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency. 2018. OpenFace 2.0: Facial Behavior Analysis Toolkit. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). 59–66. DOI: 10.1109/FG.2018.00019
Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65–72.
Igor L.O. Bastos, Michele F. Angelo, and Angelo C. Loula. 2015. Recognition of Static Gestures Applied to Brazilian Sign Language (Libras). (2015).
S. Bharti, A. Balmik, and A. Nandy. 2023. Novel error correction-based key frame extraction technique for dynamic hand gesture recognition. Neural Computing and Applications (2023).
Danielle Bragg, Oscar Koller, Mary Bellard, Larwan Berke, Patrick Boudreault, Annelies Braffort, Naomi Caselli, Matt Huenerfauth, Hernisa Kacorri, Tessa Verhoef, Christian Vogler, and Meredith Ringel Morris. 2019. Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective. In Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility (Pittsburgh, PA, USA) (ASSETS ’19). Association for Computing Machinery, New York, NY, USA, 16–31. DOI: 10.1145/3308561.3353774
Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2021. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 1 (2021), 172–186. DOI: 10.1109/TPAMI.2019.2929257
Fabio Cappabianco, Igor Ribeiro Ferreira de Matos, and Luiz Eduardo Galvão Martins. 2024. A Systematic Literature Review on Computer Vision Applied to Brazilian Sign Language. Luiz Eduardo Galvão, A Systematic Literature Review on Computer Vision Applied to Brazilian Sign Language (2024).
João Carreira and Andrew Zisserman. 2017. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4724–4733. DOI: 10.1109/CVPR.2017.502
Omdena São Paulo Chapter. 2023. Brazilian Sign Language Datasets: UFV, INES, SignBank, V-Libras. [link] BrazilianSignLanguage. Acesso em: jul. 2025.
D.R.B. da Silva, T.M.U. de Araújo, T.G. do Rêgo, L.C.S. Florêncio, A.F. Neto, and A.C. de Paiva. 2023. A multiple stream architecture for the recognition of signs in Brazilian sign language in the context of health. Multimedia Tools and Applications (2023).
Diego R. B. da Silva, Tiago Maritan U. Araujo, Thais Gaudencio do Rêgo, and Manuella Aschoff Cavalcanti Brandão. 2020. A Two-Stream Model Based on 3D Convolutional Neural Networks for the Recognition of Brazilian Sign Language in the Health Context. (2020).
Amanda Hellen de Avellar Sarmento and Moacir Antonelli Ponti. 2023. A Cross-Dataset Study on the Brazilian Sign Language Translation. (2023).
Aron Caiuá V. De Brito, Ana Patrícia F. M. Mascarenhas, Marco A.C. Simões, Ivanoé J. Rodowanski, Jorge Alberto P. De Campos, Josemar Rodrigues De Souza, and Jose Grimaldo Da Silva Filho. 2024. RoboSign: Improving Interaction Between Service-Robot and Hearing Impairments People. (2024).
Ronice Müller de Quadros. 2006. Gramática da Língua de Sinais Brasileira. Editora Bookman, Porto Alegre, Brasil. Obra de referência sobre a estrutura linguística da LIBRAS.
Universidade Federal de Santa Catarina (UFSC). 2020. Corpus Libras UFSC. [link]. Acesso em: jul. 2025.
Giely M. C. de Souza. 2019. Tradução Automática Português-Libras: Uma abordagem híbrida baseada em RBMT e NMT. Ph.D. Dissertation. Universidade Federal de Santa Catarina. [link]
Universidade Federal de Viçosa. 2020. Dicionário Online Bilíngue Libras/ Português. [link]. Acesso em: 11 ago. 2025.
Mariza Miola Dosciatti, Lohann Paterno Coutinho Ferreira, and Emerson Cabrera Paraiso. 2013. Identificando Emoções em Textos em Português do Brasil usando Máquina de Vetores de Suporte em Solução Multiclasse. In Congresso Nacional de Iniciação Científica - CONIC. Brasil. [link] Disponível em: [link].
Rodrigo Zempulski Fanucchi, Arlindo Rodrigues Galvão Junior, Gabriel da Mata Marques, Lucas Brandão Rodrigues, Anderson da Silva Soares, and Telma Woerle Lima Soares. 2024. Fine-Tuning a Video Masked Autoencoder to Develop an Augmented Reality Application for Brazilian Sign Language Interpretation. (2024).
Andrew Flores Brongar, Jordan Zitzke Pinho, Rosa Vicari, Gisele Moraes Simas, and Regina Barwaldt. 2024. Improving Translation from Portuguese to Brazilian Sign Language with Speech-to-Text Integration Through Natural Language Processing. (2024).
Silas Luiz Furtado, Jauvane C. De Oliveira, and Shervin Shirmohammadi. 2023. Interactive and Markerless Visual Recognition of Brazilian Sign Language Alphabet. (2023).
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770–778. DOI: 10.1109/CVPR.2016.90
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735
Glenn Jocher. 2020. YOLOv5 by Ultralytics. DOI: 10.5281/zenodo.3908559
Daniel Jurafsky and James H. Martin. 2019. Speech and Language Processing (3 ed.). Pearson. Draft available at [link].
H Pallab Jyoti Dutta, Debajit Sarma, M.K. Bhuyan, and R. H. Laskar. 2020. Semantic Segmentation based Hand Gesture Recognition using Deep Neural Networks. (2020).
Navroz Kaur Kahlon and Williamjeet Singh. 2023. Machine translation from text to sign language: a systematic review. Universal Access in the Information Society 22, 1 (2023), 1–35.
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2016. Autoencoding beyond pixels using a learned similarity metric. In International conference on machine learning. PMLR, 1558–1566.
Chin-Yew Lin. 2004. ROUGE: A Package for Automatic Evaluation of Summaries. In Text Summarization Branches Out: Proceedings of the ACL-04 Workshop. Association for Computational Linguistics.
Matthew J. Page, Joanne E. McKenzie, Patrick M. Bossuyt, Isabelle Boutron, Tammy C. Hoffmann, Cynthia D. Mulrow, Larissa Shamseer, Jennifer M. Tetzlaff, Elie A. Akl, Sue E. Brennan, Roger Chou, Julie Glanville, Jeremy M. Grimshaw, Asbjørn Hróbjartsson, Manoj M. Lalu, Tianjing Li, ElizabethW. Loder, Evan Mayo-Wilson, Steve McDonald, Luke A. McGuinness, Lesley A. Stewart, James Thomas, Andrea C. Tricco, and Vivian A. Welch. 2021. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372 (2021), n71. DOI: 10.1136/bmj.n71
Papers with Code. 2025. Inception-v3. [link] Licença: CC-BY-SA.
Parsifal. n.d.. About Parsifal: Perform Systematic Literature Reviews. [link]. Accessed: 2025-08-11.
Michele D. Peters, Christina M. Godfrey, Deborah McInerney, Patricia Soares, Hanan Khalil, and Beverley J. Parker. 2015. The Joanna Briggs Institute Reviewers’ Manual: Methodology for JBI Scoping Reviews. The Joanna Briggs Institute (2015). Available online: [link].
Presidência da República do Brasil. 2002. Lei nº 10.436, de 24 de abril de 2002. [link]. Recognizes the Brazilian Sign Language (Libras) as a legal means of communication and expression..
Presidência da República do Brasil. 2005. Decreto nº 5.626, de 22 de dezembro de 2005. [link]. Regulates Lei nº 10.436/2002 and mandates the provision of accessibility for deaf people in education, media, and public services..
Soraia Prietch, J. Alfredo Sánchez, and Josefina Guerrero. 2022. A Systematic Review of User Studies as a Basis for the Design of Systems for Automatic Sign Language Processing. ACM Trans. Access. Comput. 15, 4, Article 36 (Nov. 2022), 33 pages. DOI: 10.1145/3563395
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 1, Article 140 (Jan. 2020), 67 pages.
UCI Machine Learning Repository. 2008. Libras Movement Dataset. [link]. 15 classes de movimentos manuais. Acesso em: jul. 2025.
Tamires et al. Rezende. 2022. MINDS-Libras:UmConjunto Multimodal para Sinais em Libras. [link]. Acesso mediante solicitação. UFMG. Acesso em: jul. 2025.
Jampierre Rocha, Jeniffer Lensk, Taís Ferreira, and Marcelo Ferreira. 2020. Towards a Tool to Translate Brazilian Sign Language (Libras) to Brazilian Portuguese and Improve Communication with Deaf. (2020).
Ailton José Rodrigues and Emmanuel P. Barbosa. 2020. Corpus paralelo Português-Libras para tradução automática baseado em avatares 3D. [link]. Acesso em: jul. 2025.
Karl Rohr. 2001. Landmark-based image analysis: using geometric and intensity models. Vol. 21. Springer Science & Business Media.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 234–241.
Daniel Sánchez Ruiz, J. Arturo Olvera-López, and Ivan Olmos-Pineda. 2023. Word Level Sign Language Recognition via Handcrafted Features. IEEE Latin America Transactions (2023).
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4510–4520. DOI: 10.1109/CVPR.2018.00474
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Publicado
10/11/2025
Como Citar
COSTA, Victor; TOMASZEWSKI, João Pedro; PEREIRA, Lucas; SANTANA, Brenda Salenave; CORRÊA, Guilherme.
AI-Based Approaches for Brazilian Sign Language Recognition: A Systematic Literature Review: Insights on Methods, Metrics, and Resources for LIBRAS Recognition. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 31. , 2025, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 585-597.
DOI: https://doi.org/10.5753/webmedia.2025.16082.
