A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks

TL;DR

This paper introduces a transfer learning model using CNNs for handwritten numeral recognition in Hindi, Arabic, and Bangla, achieving high accuracy with reduced training time compared to existing methods.

Contribution

It presents a novel transfer learning approach with CNNs specifically designed for Indic language numerals, improving efficiency and accuracy over traditional models.

Findings

Achieved comparable accuracy to state-of-the-art methods

Reduced training time significantly

Effective transfer learning across multiple Indic languages

Abstract

Increased accuracy in predictive models for handwritten character recognition will open up new frontiers for optical character recognition. Major drawbacks of predictive machine learning models are headed by the elongated training time taken by some models, and the requirement that training and test data be in the same feature space and consist of the same distribution. In this study, these obstacles are minimized by presenting a model for transferring knowledge from one task to another. This model is presented for the recognition of handwritten numerals in Indic languages. The model utilizes convolutional neural networks with backpropagation for error reduction and dropout for data overfitting. The output performance of the proposed neural network is shown to have closely matched other state-of-the-art methods using only a fraction of time used by the state-of-the-arts.