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Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body
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Breaking CAPTCHA with Capsule Networks.

Ionela Georgiana Mocanu1, Zhenxu Yang1, Vaishak Belle1

  • 1University of Edinburgh, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|July 31, 2022
PubMed
Summary
This summary is machine-generated.

Capsule Networks, which preserve spatial relationships, show promise for challenging CAPTCHA digit recognition tasks. They offer advantages over Convolutional Neural Networks in handling distorted images.

Keywords:
CAPTCHACapsule networksConvolutional neural networksDigit recognitionSpatial invariance

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Convolutional Neural Networks (CNNs) excel at image classification but struggle with spatial feature relationships, leading to misclassifications.
  • Capsule Networks (CapsNets) were developed to address this limitation by encoding spatial hierarchies of image features.

Purpose of the Study:

  • To empirically compare the performance of Capsule Networks against Convolutional Neural Networks for digit recognition on CAPTCHA images.
  • To demonstrate the advantages and limitations of Capsule Networks architecture when dealing with distorted images common in CAPTCHAs.

Main Methods:

  • Training Capsule Networks with Dynamic Routing and a CNN-based deep-CAPTCHA baseline model.
  • Evaluating performance on numerical CAPTCHA datasets to predict digit sequences.

Main Results:

  • Capsule Networks demonstrate competitive performance against CNNs on CAPTCHA digit recognition.
  • The study highlights the effectiveness of CapsNets in preserving spatial information crucial for distorted image analysis.

Conclusions:

  • Capsule Networks offer a viable alternative to CNNs for image recognition tasks involving complex spatial relationships, such as CAPTCHA decoding.
  • The research proposes two improvements to the Capsule Networks model for enhanced CAPTCHA recognition capabilities.