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Devanagari Character Classification Using Capsule Network

  • Jeel Sukhadiya
  • Yashi SubaEmail author
  • Mitchell D’silva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Handwritten character recognition has been a widely explored topic in computer vision since its inception and exceptional works have been proposed in this area. Evolving over the primordial works in this domain, Deep Convolutional neural networks provide an epitome solution to classification of characters but still lack orientation and spatial relationships among entities. Convolutional neural networks fail in capturing the pose, orientation and view of the images due to the inefficiency of max pooling layer. These limitations are overcome by a novel approach: Capsule Networks. Capsule Networks are made up of layers of capsules representing the instantiation parameters of entities by using the dynamic routing and route by agreement algorithms.

Keywords

Capsule Network Histogram oriented gradients Support vector machines Feed forward neural networks 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jeel Sukhadiya
    • 1
  • Yashi Suba
    • 1
    Email author
  • Mitchell D’silva
    • 1
  1. 1.Department of Information TechnologyDwarkadas J. Sanghvi College of EngineeringMumbaiIndia

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