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Simplistic Deep Learning for Japanese Handwritten Digit Recognition

  • Arkadip Ghosh
  • Aishwarjyamoy Mukherjee
  • Chinmoy GhoshEmail author
Conference paper
  • 74 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)

Abstract

We show a simplistic approach using simple Convolutional Neural Network (CNN) to classify Japanese handwritten digit dataset Kuzushiji-MNIST. We use combinations of loss functions and optimisers on an empirically sound model of Convolutional Neural Netwok (CNN) to come up with a new State-of-the-art accuracy for all Simple CNN approaches on the Kuzushiji-MNIST dataset with accuracy of 96.13 %.

Keywords

Convolutional Neural Netwok (CNN) Kuzushiji-MNIST Optimizer Loss function Adam Categorical crossentropy 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Arkadip Ghosh
    • 1
  • Aishwarjyamoy Mukherjee
    • 1
  • Chinmoy Ghosh
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringJalpaiguri Government Engineering College (Autonomous)JalpaiguriIndia

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