Convolutional Neural Network
For NumtaDB Dataset our overall Validation Accuracy is 98.24%.
Confusion matrix for CNN on NumtaDB
Results of the samples from the NumtaDB
For BDRW Dataset our overall Validation Accuracy is 91.87%.
Confusion matrix for CNN on BDRW dataset.
Results of the samples from the BDRW
Stochastic Gradient Descent
For NumtaDB Dataset our overall Validation Accuracy is 92.83%.
Confusion matrix for SGD on NumtaDB
For BDRW Dataset our overall Validation Accuracy is 84.68%.
Confusion matrix for SGD on BDRW dataset.
Multi-layer Perceptron
For NumtaDB Dataset our overall Validation Accuracy is 84.49%.
Confusion matrix for MLP on NumtaDB
For BDRW Dataset our overall Validation Accuracy is 65.00%.
Confusion matrix for MLP on BDRW dataset.
K-Nearest Neighbor (KNN)
For NumtaDB Dataset our overall Validation Accuracy is 67.32%.
Confusion matrix for KNN on NumtaDB
For BDRW Dataset our overall Validation Accuracy is 77.06%.
Confusion matrix for KNN on BDRW dataset.
Random Forest Classifier
For NumtaDB Dataset our overall Validation Accuracy is 73.50%.
Confusion matrix for Random Forest on NumtaDB
For BDRW Dataset our overall Validation Accuracy is 56.66%.
Confusion matrix for Random Forest on BDRW dataset.
Overall Results
Overall Result
