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A Straightforward and Efficient Instance-Aware Curved Text Detector.

Fan Zhao1, Sidi Shao1, Lin Zhang1

  • 1Department of Information Science, Xi'an University of Technology, Xi'an 710054, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces Look More Than Twice (LOMT), an efficient curved scene text detector. LOMT accurately detects irregular text by refining bounding boxes into polygons, improving upon existing methods.

Keywords:
article swarm optimizationconvolutional neural networkscurved texttext detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Scene text detection is challenged by curved and irregular text shapes.
  • Manual annotation for curved text datasets is labor-intensive.
  • Regression-based detectors struggle with non-quadrilateral text instances.

Purpose of the Study:

  • To develop an efficient and accurate instance-aware curved scene text detector.
  • To overcome limitations of existing methods in handling irregular text.
  • To reduce the need for extensive manual annotations in training.

Main Methods:

  • Introduced Look More Than Twice (LOMT) detector.
  • Developed a curve text shape approximation module using particle swarm optimization (PSO-TSA).
  • Implemented an instance-aware component merging network (ICMN) for polygon refinement.

Main Results:

  • LOMT demonstrates excellent performance and high speed across five datasets.
  • PSO-TSA effectively optimizes text shape from quadrilateral to curved fits.
  • ICMN successfully merges incomplete text components into complete polygons.

Conclusions:

  • LOMT offers a robust solution for curved scene text detection.
  • The proposed PSO-TSA and ICMN modules significantly enhance detection accuracy and efficiency.
  • The method provides a practical alternative to manual annotation-heavy approaches.