Recognition of Handwritten Digits using Convolutional Neural Network
Author(s):
Akhil Kukkadapu, Adharsh Nandigama, K Tharun Chary, Mohd Faisal
Keywords:
handwritten digit recognition, deep learning, convolutional neural network
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recognition is a critical task with widespread applications and ranging from automated postal sorting to digitizing historical documents.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
Article Details
Unique Paper ID: 162606
Publication Volume & Issue: Volume 10, Issue 10
Page(s): 536 - 539
Article Preview & Download
Share This Article
Join our RMS
Conference Alert
NCSEM 2024
National Conference on Sustainable Engineering and Management - 2024