Abstract
Convolutional Neural Network(CNN) have achieved great success in image recognition and classification, but most of researches on gesture recognition are for English, there are very few identification studies for other small languages. An recognition method for Italian alphabet gestures based on CNN is proposed in this paper. First, an Italian alphabet gesture data set is created, including 22 letters, of which 4 letters (G, S, J, and Z) are excluded from the data set because of their dynamism, and a CNN model consisting of three blocks is established, convolutional and pooling layers are included in every block. Then weights and biases in the model are trained by the adaptive moment estimation algorithm called Adam to reduce the loss to the minimum step by step. Finally, we build a CNN model based on the proposed method on a deep learning platform named Keras, testing images are selected from the established static dataset randomly, the results of multiple experiments show that the recognition rate can reach up to 94%.
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Acknowledgement
This paper is sponsored by the 9th Key Subject Construction Project of Henan (Zhengzhou Normal University) & National Education Ministry, School and Research Cooperation Project (NEMSRC, Grant 201801329013).
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Ji, X., Yu, Q., Liu, Y., Kong, S. (2019). A Recognition Method for Italian Alphabet Gestures Based on Convolutional Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_63
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DOI: https://doi.org/10.1007/978-3-030-26763-6_63
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