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CAPTCHA recognition based on deep convolutional neural network.

Jing Wang1, Jiao Hua Qin1, Xu Yu Xiang1

  • 1College of Computer Science and Information Technology, Central South University of Forestry and Technology, 498 shaoshan S Rd, Changsha, 410004, China.

Mathematical Biosciences and Engineering : MBE
|September 11, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Dense Convolutional Network (DenseNet) for more efficient and accurate CAPTCHA recognition. The new model, DFCR, significantly enhances performance while reducing memory usage for complex CAPTCHAs.

Keywords:
CAPTCHA recognitionDenseNetResNetconvolutional neural networkdeep learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional CAPTCHA recognition methods suffer from low efficiency and poor accuracy.
  • Deep Convolutional Neural Networks (CNNs), particularly Dense Convolutional Networks (DenseNets), offer superior classification but can have high memory demands.

Purpose of the Study:

  • To develop a more efficient and accurate CAPTCHA recognition system.
  • To address the high memory consumption issue associated with DenseNets.

Main Methods:

  • An improved DenseNet for CAPTCHA recognition (DFCR) was designed by reducing convolutional blocks and creating type-specific classifiers.
  • CAPTCHA images in TFrecords format were used for DFCR model training.
  • The DFCR model was experimentally tested on Chinese and English CAPTCHAs with varying character counts.

Main Results:

  • The DFCR model retains the performance advantages of DenseNets while significantly reducing memory consumption.
  • Recognition accuracy for CAPTCHAs with background noise and character adhesion exceeded 99.9%.

Conclusions:

  • The developed DFCR model offers an effective solution for efficient and accurate CAPTCHA recognition.
  • This approach successfully balances high performance with reduced memory footprint, outperforming traditional methods.