An Efficient Method for Bangla Handwritten Digit Recognition Using Convolutional Neural Network

Indronil Bhattacharjee ()

Technium, 2023, vol. 18, issue 1, 65-74

Abstract: Handwritten digit recognition is a fundamental problem in the field of computer vision and pattern recognition. This paper presents a Convolutional Neural Network (CNN) approach for recognizing handwritten Bangla digits. The proposed method utilizes a dataset of handwritten Bangla digit images and trains a CNN model to classify these digits accurately. The dataset is preprocessed to enhance the quality of the images and make them suitable for training the CNN model. The trained model is then tested on a separate test dataset to evaluate its performance in terms of accuracy. With the Ekush: Bangla Handwritten Data - Numerals dataset, we tested our CNN implementation to determine the precision of handwritten characters. According to the test results, 25% of the images using a training set of more than 150,000 images from Ekush dataset had an accuracy of 98.3%.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://techniumscience.com/index.php/technium/article/view/10243/3987 (application/pdf)
https://techniumscience.com/index.php/technium/article/view/10243 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tec:techni:v:18:y:2023:i:1:p:65-74

DOI: 10.47577/technium.v18i.10243

Access Statistics for this article

Technium is currently edited by Scurtu Ionut Cristian

More articles in Technium from Technium Science
Bibliographic data for series maintained by Ana Maria Golita ().