# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 4 Analysis and Findings from Experiments
### Result Analysis  
This application is designed to recognize significant Bangla letters and common phrases encountered in daily life. It utilizes both the original YOLOv11 and an improved YOLOv11 model. The enhanced YOLOv11 version was employed to train the algorithm for 150 epochs using annotations formatted according to the YOLO standard. The model's performance was evaluated using precision (P), recall (R), and mean average precision (mAP), with the Intersection over Union (IoU) metric ranging from 0.5 to 0.95. In typical object detection training, two key inputs are provided: the ground-truth bounding boxes, which define the exact locations of the objects within the image, and the predicted bounding boxes generated by the model. Due to inherent model constraints and data variability, some discrepancies between the predicted and actual bounding boxes are to be expected. Evaluating the model's performance based on predicted bounding boxes can be complex, as the degree of alignment between predicted and ground-truth boxes may vary across different observations. The coordinates of its top-left corner define each bounding box and include the object's width and height dimensions along the x and y axes. Otherwise, the class is not considered positive. As the training progresses, metric graphs clearly depict slopes that illustrate the effectiveness of the proposed method in achieving accurate alphabet prediction. Table 7 shows the values of recall, accuracy, and F1 score. Furthermore, we add that for IoU thresholds of 0.5 and 0.95, the mAP scores were recorded at 0.998 and 0.989, respectively, as presented in Figure 3g.i. These results underscore the efficacy of our approach in accurately predicting the Bangla letters. In the F1 score graph, both IoU measurements at 0.5 and 0.95 yield similarly relevant outcomes. The confidence value that optimizes recall (R), as illustrated in Figure 3h, and accuracy, as depicted in Figure 3f, is 0.999. This is comparable to the best F1 score of 0.934, as shown in Figure 3e. It is generally recommended to prioritize a higher F1 score alongside a more confident prediction. The F1 score curve can be a useful tool to strike the optimal balance between precision and recall, ensuring both efficiency and accuracy. The graph in Figure 3i shows the loss, precision, recall, and mAP functions for both the training and validation sets. The first three graphs in the top-left corner represent the training loss functions, all of which consistently decrease over time, indicating steady improvement in model performance. Similarly, the three bottom-left graphs show the validation loss functions, which also exhibit a downward trend, confirming that the model's generalization is improving as training progresses. Moreover, Figure 3a-d provides a detailed view of class-level prediction behavior. The confusion matrices highlight accurate predictions and occasional misclassifications, while the class labels and correlogram curves further validate the model's robustness across different Bangla characters. Notably, the object loss function experiences a slight peak around the 20 th epoch, but this is followed by a resumption of the declining trend after a few epochs, suggesting that the model is adapting to the complexity of the data. The accuracy and recall functions show a continuous upward trajectory, further supporting the conclusion that the model is learning effectively. The mAP50 metric steadily approaches a value of one by the 150 th epoch, which signifies that the model achieves near-perfect detection performance at a 50% Intersection over Union (IoU) threshold. This indicates that as over-detection occurs, the model's detection capabilities improve, resulting in higher mAP scores. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Contrast** & **Original YOLOv11** & **Our** \\ \hline
**Precision (P)** & 98.2\% & 99.9\% \\ \hline
**Recall (R)** & 97.5\% & 1.0\% \\ \hline
**F1** & 90.3\% & 93.4\% \\ \hline \hline \end{tabular}
\end{table}