# A Real-Time Dynamic Gesture Variability Recognition Method Based on Convolutional Neural Networks
## 4 Results
Figure 7: Confusion matrix of the dynamic gesture variability recognition model for all test groups (see Table 1 for Kazakh’s words translations). The validation accuracy 0.92 is an important metric, as it provides an estimate of how well the model is generalizing to new, unseen data. A high validation accuracy indicates that the model is performing well and can be trusted to make accurate predictions for new data. The proposed method for sign language recognition was tested on three different datasets (Table 1, and Figure 8): the Ankara University Turkish Sign Language Dataset (AUTSL), the Dataset for Argentinian Sign Language (LSA64), and Kazakh Sign Language (KSL). Table 1 presents a comparison of the performance of the proposed method for sign language recognition with other works based on three different datasets: AUTSL, LSA64, and the author's dataset: KSL. The results show that the proposed method has a higher precision, recall, F1 score, and accuracy than the other works for the KSL dataset. The precision, recall, F1 score, and accuracy values were all 0.98, indicating that the proposed method is highly accurate in recognizing signs in this dataset. For the AUTSL dataset, the precision, recall, F1 score, and accuracy values were all 0.93. This indicates that the proposed method performs well in recognizing signs in this dataset, although not as well as in the KSL dataset. For the LSA64 dataset, the precision, recall, F1 score, and accuracy values were all 1. This indicates that the proposed method performs perfectly in recognizing signs in this dataset (Figure 8). \begin{table}
\begin{tabular}{c c c c c} \hline
**Datasets** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline AUTSL & 0.93 & 0.93 & 0.93 & 0.93 \\ \hline LSA64 & 1 & 1 & 1 & 1 \\ \hline KSL & 0.98 & 0.98 & 0.98 & 0.98 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the authors’ method with other work (only RGB) on a test set of the AUTSL, LSA64, and KSL datasets. Figure 8: Confusion matrix of the AUTSL and LSA64 datasets (see Table A1 for Kazakh’s words translations). Overall, the results suggest that the proposed method is effective in recognizing signs in multiple datasets and has a higher accuracy than other works in recognizing the signs in the KSL dataset. Table 2 presents a comparison of the recognition results achieved using different gesture language recognition methods on the AUTSL dataset, including hybrid and ensemble architectures. The proposed method in the study achieved an accuracy of 0.98, which was slightly lower than the results achieved by other state-of-the-art methods, such as STF+LSTM and 3D CNN, CGN, and RGB-D, which achieved accuracies of 0.9856 and 0.9853, respectively. It is noteworthy that the proposed approach does not rely on complex hybrid architectures or ensemble models that demand significant computational resources. Despite this, the method achieves a reasonable level of recognition accuracy without the need for supplementary features such as text or lip data, or additional equipment like depth-sensing cameras or gloves. These findings suggest that gesture recognition can be accomplished using simpler architectures and only RGB images, indicating promising possibilities for practical applications. The comparison table highlights the potential of more complex architectures and depth cameras for achieving higher accuracy in gesture recognition, as shown by some of the state-of-the-art methods presented. However, the proposed method is still effective in achieving acceptable recognition accuracy without using these more complex techniques, which suggests that it may be a useful and practical solution in certain contexts.