Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{162606,
        author = {Akhil Kukkadapu and Adharsh Nandigama and K Tharun Chary and Mohd Faisal},
        title = {Recognition of Handwritten Digits using Convolutional Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {536-539},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162606},
        abstract = {Handwritten digit recognition is a critical task with widespread applications and ranging from automated postal sorting to digitizing historical documents. In this paper and we propose a robust approach for handwritten digit recognition leveraging Convolutional Neural Networks (CNNs). CNNs have dеmonstratеd exceptional performance in image related tasks and making them well suited for thе intricatе patterns present in handwritten digits. Our methodology involves the construction of a dееp neural network architecture specifically designed for thе complexities of handwritten digit recognition. Thе proposed model employs multiple convolutional layers to capture hierarchical features of thе input images and followed by pooling layers for spatial dimension reduction. Additionally, and fully connected layers are incorporated to enable global learning and feature integration},
        keywords = {handwritten digit recognition, deep learning, convolutional neural network},
        month = {},
        }