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Text Font Correction and Alignment Method for Scene Text Recognition.

Liuxu Ding1, Yuefeng Liu1, Qiyan Zhao1

  • 1School of Digital and Intelligent Industry, Inner Mongolia University of Science and Technology, Baotou 014010, China.

Sensors (Basel, Switzerland)
|January 8, 2025
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Summary

This study introduces novel methods for robust text recognition, improving accuracy for arbitrary-shaped and occluded text in natural scenes. The developed Discriminative Standard Text Font (DSTF) and Feature Alignment and Complementary Fusion (FACF) enhance text recognition performance.

Keywords:
attentionfeature fusionscene text recognitiontext font alignment

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Text recognition in natural scenes is challenging due to irregular text shapes, fonts, and occlusions.
  • Existing methods struggle with the variability and complexity of real-world text images.

Purpose of the Study:

  • To develop an improved text recognition system capable of handling arbitrary-shaped text, irregular fonts, and occlusions.
  • To introduce a new dataset, the VBC Chinese dataset, for evaluating text recognition under diverse lighting conditions.
  • To enhance the accuracy and robustness of text recognition in natural scene images.

Main Methods:

  • Designed Discriminative Standard Text Font (DSTF) for handling irregular text fonts.
  • Developed Feature Alignment and Complementary Fusion (FACF) for arbitrary-shaped text.
  • Proposed Dual Attention Serial Module (DASM) to address text occlusion by enhancing focus on text texture.
  • Created the VBC Chinese dataset with varying lighting conditions (strong light, weak light, darkness).

Main Results:

  • The proposed method achieved 90.8% accuracy on the VBC Chinese dataset.
  • An overall average accuracy of 93.8% was obtained, demonstrating competitive performance.
  • The integrated components effectively corrected irregular text and improved feature alignment.

Conclusions:

  • The novel approach significantly improves text recognition accuracy and robustness in challenging natural scenes.
  • The developed methods and dataset contribute to advancing the field of scene text recognition.
  • The system demonstrates strong performance under diverse and adverse lighting conditions.