## 4 Results
### Comparison with Base Paper Results  
It is evident from Table 14 that in the case of the Cambridge dataset, whose hand gestures are devoid of any cluttered backdrop, using a convolutional-related feature such as CNN features on hand edges with an ensemble classifier such asXGBoost, gives increased accuracy compared to a CNN with softmax classification as described in [PERSON], [PERSON] et al. [6]. This is because by nature ensembles bolster the predictive performance by decreasing variance of the predictive process with the aid of multiple classifiers each of which adds bias. A single CNN, on the other hand, is limited by its specific architecture and count of epochs to achieve only a certain level of accuracy. [PERSON], [PERSON] et al. [6] could have obtained better results with an ensemble of CNNs. Secondly, the usage of hand coordinates \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **User A** & **User B** & **User C** & **User D** & **User E** &
\begin{tabular}{c} **Average** \\ **Accuracy** \\ \end{tabular} \\ \hline
**Hand coordinates + Random Forest** & 85.472\% & 85.695\% & 93.436\% & 85.588\% & 92.222\% & 88.483\% \\
**Hand coordinates + XGBoost** & 87.916\% & 88.780\% & 94.789\% & 87.276\% & 92.387\% & 90.230\% \\
**Hand coordinates + ANN** & 92.611\% & 90.687\% & 95.028\% & 91.689\% & 92.676\% & 92.538\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Leave-one-out-accuracies. Figure 13: Diagnostic curves for CNN-NUS II dataset. pinpoints exact locations of the regions of interest of the hand gesture for better mapping between input features and target label compared to a CNN which requires meticulous hyperparameter tuning to capture the exact same features. It can also be seen that the hybrid ANN outperforms the base paper CNN since it takes into consideration both the actual image and the edge information of the gestures to categorize the sign images. Table 14 also highlights the fact that for the ASL finger-spelling dataset, to test leave-one-out-accuracy, hand coordinates transcend the convolutional features from a PCANet model as specified in [PERSON], [PERSON] et al. [8]. This is because despite user-specificity, when using hand coordinates, the relative distance between the hand joints for the same sign remain consistent across all users, whereas in [PERSON], [PERSON] et al. [8], some of this information is lost since the PCANet model uses PCA to select its convolutional filters. Moreover, in [PERSON], [PERSON] et al. [8], the PCANet seems to be generating features greater than the number of instances in the dataset. This causes the SVM to get stuck in a particular hyperplane without advancing any further which could have given better separation between image classes. Ensemble classifiers and ANN do not suffer from this problem. For the ISL digits in Table 14, the usage of depth images as mentioned in [PERSON] [13] is causing the hand gesture to be a white region amidst a black environment. This is leading to some information loss in terms of sign-specific hand features for the ORB algorithm to detect valuable keypoints. The proposed technique, however, gets rid of depth images in favour of hand coordinates which provides exact location of hand region and convolutional features on the original image as well as the edge image so that the exact hand outline can be learned by the ensemble classifiers and the hybrid ANN. These factors assist in better separation between the digits.