Abstract
This research aims to develop a lip gesture recognition system in Arabic words by utilizing Histogram of Oriented Gradient (HOG) feature extraction and Support Vector Machine (SVM) classification. The evaluation was conducted on a dataset of 1749 videos with male and female participation using Modern Standard Arabic. The 10 cross-fold validation method was used to measure the performance of the system. By applying a polynomial kernel, this study achieved an accuracy rate of 95.63%, while the word recognition rate reached 96%. These results confirm the system's ability to recognize lip movements with precision, confirming the effectiveness of the approach used in visual recognition for Arabic.
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