Abstract
Currently there are many techniques and methods continuously proposed by researchers for Sign language recognition systems based-on machine learning. For data preprocessing for sign language, majority of researchers use single image of hands like static gesture images. Using only static hand images may not be efficient for real-world applications. In this paper, we propose an innovative technique for video processing called Sequenced Edge Grid Images (SEGI) for Sign Language recognition. The proposed SEGI is composed of images that represent the movement of hands within a single image, which can be applied to recognize a word or a sentence. To proof the concept, we have done several experiments with Thai sign language data collected from internet. SEGI was with existing techniques. Data are the Thai sign language learning video clips that are vocabularies to use in daily life. The proposed technique was implemented with convolutional neural network (CNN). For normal CNN, the experiments show that the SEGI with CNN increases of test accuracy rate with approximately 27% when compared to static gesture images.
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References
Cooper, H., Ong, E.-J., Pugeault, N., Bowden, R.: Sign language recognition using sub-units. In: Escalera, S., Guyon, I., Athitsos, V. (eds.) Gesture Recognition. TSSCML, pp. 89–118. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57021-1_3
Zadghorban, M., Nahvi, M.: An algorithm on sign words extraction and recognition of continuous Persian sign language based on motion and shape features of hands. Pattern Anal. Appl. 21(2), 323–335 (2016). https://doi.org/10.1007/s10044-016-0579-2
Rastgoo, R., Kiani, K., Escalera, S.: Hand sign language recognition using multi-view hand skeleton. Expert Syst. Appl. 150, 1–12 (2020)
Wu, J., Sun, L., Jafari, R.: A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE J. Biomed. Health Inform. 20(5), 1281–1290 (2016)
Khomami, S.A., Shamekhi, S.: Persian sign language recognition using IMU and surface EMG sensors. Measurement 168, 108471 (2021)
Gupta, R., Kumar, A.: Indian sign language recognition using wearable sensors and multi-label classification. Comput. Electr. Eng. 90, 106898 (2020)
Fan, J., Xu, W., Wu, Y., Gong, Y.: Human tracking using convolutional neural networks. IEEE Trans. Neural Netw. 21(10), 1610–1623 (2010)
Cui, R., Liu, H., Zhang, C.: A deep neural framework for continuous sign language recognition by iterative training. IEEE Trans. Multimedia 21(7), 1880–1891 (2019)
Song, N., Yang, H., Wu, P.: A gesture-to-emotional speech conversion by combining gesture recognition and facial expression recognition. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), pp. 1–6 (2018)
Saengsri, S., Niennattrakul, V., Ratanamahatana, C.A.: TFRS: Thai finger-spelling sign language recognition system. In: 2012 Second International Conference on Digital Information and Communication Technology and It’s Applications, pp. 457–462. IEEE Xplore (2012)
Cui, R., Liu, H., Zhang, C.: Recurrent convolutional neural networks for continuous sign language recognition by staged optimization. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1610–1618. IEEE Xplore (2017)
Neyra-Gutiérrez, A., Shiguihara-Juárez, P.: Feature extraction with video summarization of dynamic gestures for peruvian sign language recognition. In: 2020 IEEE XXVII International Conference on Electronics on Electrical Engineering and Computing (INTERCON), pp. 1–4. IEEE Xplore (2020)
Prathusha, P., Jyothi, S., Mamatha, D.M.: Enhanced image edge detection methods for crab species identification. In: 2018 International Conference on Soft-Computing and Network Security (ICSNS), pp. 1–7. IEEE Xplore (2018)
Fujimoto, T., Kawasaki, T., Kitamura, K.: Canny-Edge-Detection/Rankine-Hugoniot-conditions unified shock sensor for inviscid and viscous flows. J. Comput. Phys. 396, 264–279 (2019)
Ji, S., Xu, W., Yang, M., Yu, K.: 3D Convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)
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Satiman, S., Meesad, P. (2021). A Sequenced Edge Grid Image Technique for Sign Language Recognition. In: Meesad, P., Sodsee, D.S., Jitsakul, W., Tangwannawit, S. (eds) Recent Advances in Information and Communication Technology 2021. IC2IT 2021. Lecture Notes in Networks and Systems, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-79757-7_18
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DOI: https://doi.org/10.1007/978-3-030-79757-7_18
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