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Full-Text Articles in Engineering
Uzbek Commands Recognition By Processing The Spectrogram Image, M M. Musayev, I Sh Khujayorov, M I. Abdullaeva, M M. Ochilov
Uzbek Commands Recognition By Processing The Spectrogram Image, M M. Musayev, I Sh Khujayorov, M I. Abdullaeva, M M. Ochilov
Technical science and innovation
This paper describes the most common algorithms with image approach convolutional neural network and two-dimensional DCT with machine learning classification KNN, SVM and RF. These algorithms are evaluated for applicability to the Uzbek language and a comparative analysis on the accuracy and recognition rate. The command words of the Uzbek language were chosen for the experiments. According to the results, it was found that both methods give high rates of recognition accuracy and are 92% (CNN) and 90% (2DDCT+Zigzag+SVM). Also the combinations of 2D-DCT+Zigzag+ KNN and 2D-DCT+Zigzag+ RF with average recognition accuracy of 86% and 85%, respectively, were considered in …
Static Hand Gesture Recognition Of Indonesian Sign Language System Based On Backpropagation Neural Networks, Farida Asriani, Hesti Susilawati
Static Hand Gesture Recognition Of Indonesian Sign Language System Based On Backpropagation Neural Networks, Farida Asriani, Hesti Susilawati
Makara Journal of Technology
Static Hand Gesture Recognition of Indonesian Sign Language System Based on Backpropagation Neural Networks. The main objective of this research is to perform pattern recognition of static hand gesture in Indonesian sign language. Basically, pattern recognition of static hand gesture in the form of image had three phases include: 1) segmentation of the image that will be recognizable form of the hands and face, 2) feature extraction and 3) pattern classification. In this research, we used images data of 15 classes of words static. Segmentation is performed using HSV with a threshold filter based on skin color. Feature extraction performed …