  This paper presents an analysis of pre-trained models to recognize
handwritten Devanagari alphabets using transfer learning for Deep Convolution
Neural Network (DCNN). This research implements AlexNet, DenseNet, Vgg, and
Inception ConvNet as a fixed feature extractor. We implemented 15 epochs for
each of AlexNet, DenseNet 121, DenseNet 201, Vgg 11, Vgg 16, Vgg 19, and
Inception V3. Results show that Inception V3 performs better in terms of
accuracy achieving 99% accuracy with average epoch time 16.3 minutes while
AlexNet performs fastest with 2.2 minutes per epoch and achieving 98\%
accuracy.
