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Multi-Directional Scene Text Detection Based on Improved YOLOv3.

Liyun Xiao1, Peng Zhou2, Ke Xu3

  • 1Institute of Engineering Technology, University of Science and Technology Beijing, Beijing 100083, China.

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Summary
This summary is machine-generated.

This study introduces an improved YOLOv3 algorithm for multi-directional text detection, enhancing accuracy and speed. The new method effectively detects text in natural scenes, overcoming challenges of close alignment and varied orientations.

Keywords:
CIOUYOLOv3multi-directional text detectionnatural scenes

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Existing text detection algorithms struggle with low detection rates due to closely aligned and multi-directional text.
  • There is a need for faster and more accurate algorithms for text detection in natural scenes.

Purpose of the Study:

  • To propose a novel multi-directional text detection algorithm based on an improved YOLOv3 architecture.
  • To enhance the accuracy and speed of text detection in challenging natural scene conditions.

Main Methods:

  • Introduced a sliding vertices-based method for defining text bounding boxes in multiple orientations.
  • Developed a novel rotating box loss function (MD-Closs) utilizing CIOU to improve detection precision.
  • Implemented a step-by-step Non-Maximum Suppression (NMS) to optimize computational efficiency.

Main Results:

  • Achieved an accuracy rate of 86.2% and a recall rate of 81.9% on the ICDAR 2015 dataset.
  • Demonstrated a detection speed of 21.3 frames per second (fps), indicating significant timeliness improvements.
  • The proposed algorithm shows a strong detection effect for text in natural scenes.

Conclusions:

  • The improved YOLOv3-based multi-directional text detection algorithm effectively addresses limitations of existing methods.
  • The combination of sliding vertices, MD-Closs, and step-by-step NMS leads to superior accuracy and speed.
  • This algorithm shows significant promise for practical applications in natural scene text detection.