Devanagari Handwritten Character Recognition using Convolutional Neural Network

TL;DR

This paper presents a convolutional neural network-based method for recognizing handwritten Devanagari characters, achieving high accuracy and demonstrating effectiveness on an open dataset.

Contribution

The work introduces a CNN architecture tailored for Devanagari handwritten character recognition, improving recognition rates over previous methods.

Findings

Achieved 96.36% accuracy on test data

Used a dataset with 36 classes and 1700 images per class

Demonstrated high training and testing accuracy

Abstract

Handwritten character recognition is getting popular among researchers because of its possible applications in facilitating technological search engines, social media, recommender systems, etc. The Devanagari script is one of the oldest language scripts in India that does not have proper digitization tools. With the advancement of computing and technology, the task of this research is to extract handwritten Hindi characters from an image of Devanagari script with an automated approach to save time and obsolete data. In this paper, we present a technique to recognize handwritten Devanagari characters using two deep convolutional neural network layers. This work employs a methodology that is useful to enhance the recognition rate and configures a convolutional neural network for effective Devanagari handwritten text recognition (DHTR). This approach uses the Devanagari handwritten character dataset (DHCD), an open dataset with 36 classes of Devanagari characters. Each of these classes has 1700 images for training and testing purposes. This approach obtains promising results in terms of accuracy by achieving 96.36% accuracy in testing and 99.55% in training time.