A solution for the problems of the finger spelling alphabet of gesture language modelling and recognition based on cross-platform technologies is proposed. Modelling and recognition performance can be flexible and adjusted, based on the hardware it operates or based on the availability of an internet connection. The proposed approach tunes the complexity of the 3D hand model based on the CPU type, amount of available memory and internet connection speed. Sign recognition is also performed using cross-platform technologies and the tradeoff in model size and performance can be adjusted.  the methods of convolutional neural networks are used as tools for gestures of alphabet recognition. For the gesture recognition experiment, a dataset of 50,000 images was collected, with 50 different hands recorded, with almost 1,000 images per each person. The experimental researches demonstrated the effectiveness of proposed approaches.


cross platform; sign language; fingerspelling alphabet; 3D modeling; Convolutional Neural Networks

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Published : 2019-06-21

Kondratiuk, S., Krak, I., & Wójcik, W. (2019). СROSS PLATFORM TOOLS FOR MODELING AND RECOGNITION OF THE FINGERSPELLING ALPHABET OF GESTURE LANGUAGE. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 9(2), 24-27. https://doi.org/10.5604/01.3001.0013.2542

Serhii Kondratiuk 
Taras Shevchenko National University of Kyiv  Ukraine
Iurii Krak  krak@univ.kiev.ua
1Taras Shevchenko National University of Kyiv, 2Glushkov Institute of Cybernetics of NAS of Ukraine  Ukraine
Waldemar Wójcik 
Lublin Univeristy of Technology, Institute of Electronics and Computer Science  Poland