Recognition of Handwritten Digits using Convolutional Neural Network
Akhil Kukkadapu, Adharsh Nandigama, K Tharun Chary, Mohd Faisal
handwritten digit recognition, deep learning, convolutional neural network
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recognition is a critical task with widespread applications and ranging from automated postal sorting to digitizing historical documents.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
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