Transfer learning using CNN for handwritten Devanagari character recognition
Published in 2019 1st International Conference on Advances in Information Technology (ICAIT), 2019
Recommended citation: Nagender Aneja, Sandhya Aneja "Transfer learning using CNN for handwritten Devanagari character recognition." 2019 1st International Conference on Advances in Information Technology (ICAIT), 2019. pp. 293--296 doi: 10.1109/ICAIT47043.2019.8987286 https://ieeexplore.ieee.org/abstract/document/8987286
(Conference Publication)
Abstract: This paper analyzes pre-trained models to recognize handwritten Devanagari alphabets using Deep Convolution Neural Network(DCNN) transfer learning. This research implements AlexNet, DenseNet, Vgg, and Inception ConvNet as a fixed feature extractor. We implemented 15 epochs for each AlexNet, DenseNet 121, DenseNet 201, Vgg 11, Vgg 16, Vgg 19, and Inception V3. Results show that Inception V3 performs better, achieving 99% accuracy with an average epoch time of 16.3 minutes, while AlexNet performs fastest with 2.2 minutes per epoch and achieving 98%accuracy.