Reconhecimento de Texto para Sistemas Air Writing: Um Estudo Experimental
Resumo
Este estudo explora o Air Writing (AW) como uma interface humano-máquina sem contato para entrada de texto, avaliando sua viabilidade com Reconhecimento Óptico de Caracteres (OCR). O AW permite que os usuários escrevam no ar sem superfícies físicas, apresentando desafios para a precisão de reconhecimento e detecção de intenção do usuário. Experimentos quantitativos utilizando algoritmos de OCR de código aberto em dados simulados de AW demonstram resultados promissores, especialmente com a implementação de técnicas de suavização de traços. Esta pesquisa oferece insights valiosos para melhorar a praticidade do AW e o desempenho do OCR, com o objetivo de aprimorar sua usabilidade em diversas aplicações interativas.
Referências
Abir, F. A., Siam, M. A., Sayeed, A., Hasan, M. A. M., and Shin, J. (2021). Deep learning based air-writing recognition with the choice of proper interpolation technique. Sensors, 21(24).
Bashir, M., Scharfenberg, G., and Kempf, J. (2011). Person authentication by handwriting in air using a biometric smart pen device. In BIOSIG 2011 – Proceedings of the Biometrics Special Interest Group, pages 219–226. Gesellschaft für Informatik e.V., Bonn.
Chen, M., AlRegib, G., and Juang, B.-H. (2016). Air-writing recognition—part i: Modeling and recognition of characters, words, and connecting motions. IEEE Transactions on Human-Machine Systems, 46(3):403–413.
Elshenaway, A. R. and Guirguis, S. K. (2021). On-air hand-drawn doodles for iot devices authentication during covid-19. IEEE Access, 9:161723–161744.
Itaguchi, Y., Yamada, C., and Fukuzawa, K. (2015). Writing in the air: Contributions of finger movement to cognitive processing. PLOS ONE, 10(6):1–17.
Lee, S.-K. and Kim, J.-H. (2021). Air-text: Air-writing and recognition system. In Proceedings of the 29th ACM International Conference on Multimedia, MM ’21, page 1267–1274, New York, NY, USA. Association for Computing Machinery.
Li, M., Lv, T., Cui, L., Lu, Y., Florencio, D., Zhang, C., Li, Z., and Wei, F. (2021). Trocr: Transformer-based optical character recognition with pre-trained models. [link].
Mukherjee, S., Ahmed, S. A., Dogra, D. P., Kar, S., and Roy, P. P. (2019). Fingertip detection and tracking for recognition of air-writing in videos. Expert Systems with Applications, 136:217–229.
Vaidya, V., Pravanth, T., and Viji, D. (2022). Air writing recognition application for dyslexic people. In 2022 International Mobile and Embedded Technology Conference (MECON), pages 553–558.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Vloison, V. and Xiwei, H. (2021). Deep learning framework for line-level handwritten text recognition. [link].
Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C.-L., and Grundmann, M. (2020). Mediapipe hands: On-device real-time hand tracking.
Bashir, M., Scharfenberg, G., and Kempf, J. (2011). Person authentication by handwriting in air using a biometric smart pen device. In BIOSIG 2011 – Proceedings of the Biometrics Special Interest Group, pages 219–226. Gesellschaft für Informatik e.V., Bonn.
Chen, M., AlRegib, G., and Juang, B.-H. (2016). Air-writing recognition—part i: Modeling and recognition of characters, words, and connecting motions. IEEE Transactions on Human-Machine Systems, 46(3):403–413.
Elshenaway, A. R. and Guirguis, S. K. (2021). On-air hand-drawn doodles for iot devices authentication during covid-19. IEEE Access, 9:161723–161744.
Itaguchi, Y., Yamada, C., and Fukuzawa, K. (2015). Writing in the air: Contributions of finger movement to cognitive processing. PLOS ONE, 10(6):1–17.
Lee, S.-K. and Kim, J.-H. (2021). Air-text: Air-writing and recognition system. In Proceedings of the 29th ACM International Conference on Multimedia, MM ’21, page 1267–1274, New York, NY, USA. Association for Computing Machinery.
Li, M., Lv, T., Cui, L., Lu, Y., Florencio, D., Zhang, C., Li, Z., and Wei, F. (2021). Trocr: Transformer-based optical character recognition with pre-trained models. [link].
Mukherjee, S., Ahmed, S. A., Dogra, D. P., Kar, S., and Roy, P. P. (2019). Fingertip detection and tracking for recognition of air-writing in videos. Expert Systems with Applications, 136:217–229.
Vaidya, V., Pravanth, T., and Viji, D. (2022). Air writing recognition application for dyslexic people. In 2022 International Mobile and Embedded Technology Conference (MECON), pages 553–558.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Vloison, V. and Xiwei, H. (2021). Deep learning framework for line-level handwritten text recognition. [link].
Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C.-L., and Grundmann, M. (2020). Mediapipe hands: On-device real-time hand tracking.
Publicado
17/10/2024
Como Citar
BARBOSA, Carlos E. S.; PEREIRA, Thiago B.; CARMO, Israel M. do; TELLO, Richard J. M. G.; BOLDT, Francisco A.; PAIXÃO, Thiago M..
Reconhecimento de Texto para Sistemas Air Writing: Um Estudo Experimental. In: ESCOLA REGIONAL DE INFORMÁTICA DO ESPÍRITO SANTO (ERI-ES), 9. , 2024, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 21-30.
DOI: https://doi.org/10.5753/eries.2024.244365.
