# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 4 Results and Discussion
Notably, digit 6 achieves a strong F1-Score of 0.9091, suggesting a balanced performance across both precision and recall, even though the precision and recall do not peak compared to other digits.These results highlight the effectiveness of the model across different classes. The consistency in performance metrics indicates that the model is reliable and performs well in distinguishing between all numeral categories. Confusion matrix of the results is given in Figure 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Numbers** & **Precision** & **Recall** & **F1 Score** \\ \hline
0 & 0.8830 & 0.9651 & 0.9222 \\
1 & 0.9004 & 0.9457 & 0.9225 \\
2 & 0.9179 & 0.8120 & 0.8617 \\
3 & 0.8986 & 0.8333 & 0.8647 \\
4 & 0.8767 & 0.8205 & 0.8477 \\  
The confusion matrix depicted in Figure 9 illustrates the ensemble learning model's performance in classifying the digits 0 through 9. The diagonal elements, representing the correct predictions for each class, are relatively high, indicating strong overall accuracy. Notably, class 0 and class 7 exhibit the highest accuracy, with 225 correctly predicted samples. Class 9 also performs well with 222 correctly classified instances. Some misclassifications can be observed off the diagonal. For example, class 2 shows misclassifications across several neighboring classes, including 5, 6, and 3, with a noticeable number of samples being incorrectly classified as these digits. Similarly, class 5 demonstrates some difficulty, with multiple samples being misclassified as class 1 and class 6. This suggests that there may be a higher level of ambiguity or feature overlap among these classes, potentially making them harder for the model to distinguish. To address this issue and reduce the misclassification rate between these classes, a more comprehensive dataset could be created. In particular, the learning capacity of the model on these classes can be increased by including more examples of these classes. In addition, data obtained from different angles and under varying lighting conditions can be added to help the model learn wider variations. Another method is to use more sophisticated architectures such as attention mechanisms or feature extraction layers to help the model better discriminate overlapping features between classes. In particular, solutions such as language model integration for monosyllabic digits with similar lip movements can reduce ambiguity between classes by providing context information. These approaches can be effective for improving the performance of the model and minimizing the misclassification rate between the classes in question. The confusion matrix highlights that while the ensemble learning model performs well overall, further refinement may be necessary to improve performance on classes with higher misclassification rates. Addressing these areas of confusion, particularly between digits like 2, 5, and 6, could further enhance the model's robustness and accuracy in distinguishing among all digit classes. When evaluating the digits with high accuracy rates, it can be observed that digits with multiple syllables are more easily detected.