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Robust table recognition for printed document images.

Qiao Kang Liang1,2, Jian Zhong Peng1,2, Zheng Wei Li3

  • 1College of Electrical and Information Engineering, Hunan University, Changsha 410082, China.

Mathematical Biosciences and Engineering : MBE
|September 29, 2020
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Summary
This summary is machine-generated.

This study introduces a robust table recognition system for printed documents, improving accuracy for distorted and blurred images. The developed app converts images to editable text in real time, enhancing document analysis.

Keywords:
binarization algorithmcharacter recognitiondeep learningrecurrent neural networktable image recognition

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Area of Science:

  • Computer Vision
  • Pattern Recognition
  • Image Processing

Background:

  • Table recognition from document images is crucial but existing methods lack robustness, especially with irregular or low-quality inputs.
  • Current approaches often require high regularity and struggle with distortions and blur, limiting practical applications.

Purpose of the Study:

  • To develop a robust table recognition system for printed document images.
  • To improve the accuracy and real-time processing capabilities of table recognition, particularly for low-quality images.
  • To create a practical application for transforming document images into editable text.

Main Methods:

  • The system employs image preprocessing techniques.
  • Cell location is achieved using contour mutual exclusion.
  • Printed Chinese character recognition utilizes a deep learning network.

Main Results:

  • A table recognition application was developed and tested on 105 images.
  • The system effectively identifies high-quality tables.
  • A recognition rate of 81% was achieved for low-quality tables with distortion and blur, surpassing existing methods.

Conclusions:

  • The proposed robust table recognition system demonstrates significant improvements, especially for challenging image qualities.
  • The developed application offers real-time conversion of document images to editable text.
  • This research provides valuable insights into advanced table recognition and analysis algorithms for practical use.