Transfer Learning using CNN for Handwritten Devanagari Character Recognition
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.
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Methods
1x1 Convolution •
AlexNet •
Average Pooling •
Batch Normalization •
Concatenated Skip Connection •
Convolution •
Dense Block •
Dense Connections •
DenseNet •
Dropout •
Global Average Pooling •
Grouped Convolution •
Kaiming Initialization •
Local Response Normalization •
Max Pooling •
ReLU •
Softmax •
VGG
