Transfer learning using Pre-trained AlexNet for Marathi Handwritten Compound Character Image Classification
Vrushali T. Lanjewar, Ratnashil N. Khobragade · 2021 International Conference on Intelligent Technologies (CONIT) · 2021
Transfer learning uses to train the data faster and avoid over-fitting when the size of the dataset is small. The purpose of this work is to investigate Handwritten Marathi Compound Characters and Handwritten Marathi 0-9 digits using Pre-trained Convolutional Neural Network. In this article, AlexNet mainly used to train the handwritten compound characters of Marathi Script for image classification. The tests conducted on Marathi Handwritten characters with 3,283 sample images of three compound characters, 400 images of two compound character and 1,000 Marathi Handwritten numbers with some preprocessing of resizing images into 227x 227 pixels. For three combining characters AlexNet gives the maximum accuracy of 96.77%, 97.3% and for Marathi handwritten digits gives 100% accuracy.