## 5 Result and Discussion
### Performance Evaluation with 38 BdSL Dataset  
In the experiment, the performance of the 38 BdSL datasets is evaluated on the fine-tuned of BenSignNet model with CCE loss function. According to Section 5.1, the test set of the 38 BdSL was 30%, which is divided equally into validation and test set to \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Before Augmentation**} & \multicolumn{2}{c}{**After Augmentation**} \\ \cline{2-5}  & **Train** & **Test** & **Train** & **Test** \\ \hline
38 BdSL [11] & 8512 & 3648 & 68,096 & 3648 \\ \hline KU-BdSL [12] & 1050 & 450 & 15,750 & 450 \\ \hline Ishara-Lipi [13] & 1260 & 540 & 18,900 & 540 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training and Testing Images for Each Dataset. compare with the existing method. However, the proposed system with segmented and non-segmented datasets achieved 94.00% and 93.20% accuracy for test dataset. Table 4 shows the performance accuracy of the proposed model for training, validation and test dataset. Figure 8 shows the class-wise alphabet recognition bar chart for our proposed system: precision, recall, f1-score and performance accuracy of the 38 classes of the 38 BdSL alphabets dataset. Our model produced high-performance accuracy in all the classes by observing the class-wise figure, excluding two classes. Confusion matrix of detection of the BdSL alphabet signs using the proposed system shown in Figure 9. Here, probabilities along the main diagonal or correct detection rate are defined as predicted and output classes. Alphabets sign in a predicted class represents each row of the confusion matrix, while each column represents one in a correct class. The proposed model produces good accuracy for all classes and correctly classified almost more than 90% except four classes. Among them are some misclassification, like 35% misclassification in (22, 3) and 15% misclassification in (13, 18). This happened because some signs are almost similar, so the classifier may face difficulties differentiating them. Table 5 shows the comparison accuracy of the proposed method with the existing model using 38 BdSL datasets. The proposed system produced 94.00% accuracy, which is better than the state-of-the-art method. The authors in [11] used VGG19 based CNN to recognize the BSL alphabets. They have trained their model using SGD optimizer with the learning rate 0.001 and achieved 89.60% accuracy. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Segmented** & **Training (\%)** & **Validation (\%)** & **Testing (\%)** \\ \hline
38 BdSL alphabets & no & 98.00 & 95.00 & 93.20 \\ \hline
38 BdSL alphabets & yes & 99.99 & 96.00 & 94.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training, Validation and Test accuracy of the Proposed System. Figure 8: Class wise Precision, Recall, F1-score and Accuracy of the Proposed method. The authors [33], proposed a concatenated BdSL network to recognize the 38 BdSL sign language alphabets and achieved 91.52% testing accuracy. Firstly, they resized the image in 64 \(\times\) 64; they selected a CNN architecture to extract the visual feature. They combined the CNN based feature with the hand pose estimation base feature. Their CNN architecture included ten convolutions, 10 ReLu activation, four max-pooling, and a single input-output layer. In the hand pose estimation, they have calculated the hand key points from the image. Then the CNN and hand pose feature vector passed through two fully connected layers and finally used the softmax activation function. Figure 10 shows the class-wise comparison accuracy of the proposed model with the existing model. By observing the figure, we can decide that our proposed model produced higher performance accuracy for all the classes than the existing model class wise