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Structured Cluster Detection from Local Feature Learning for Text Region Extraction.

Huei-Yung Lin1, Chin-Yu Hsu2

  • 1Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106, Taiwan.

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|May 16, 2023
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Summary
This summary is machine-generated.

This study introduces a novel technique for detecting structured regions in images by using local image features and clustering. The method effectively identifies text clusters, even with sparse feature points, for applications like invoice and banknote analysis.

Keywords:
machine visionstructure pattern analysistext region detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Region of interest detection is crucial for information extraction in machine vision.
  • Existing methods struggle with sparse feature points in character recognition.
  • Need for robust techniques for structured region detection in complex documents.

Purpose of the Study:

  • To propose a new technique for structured region detection using feature distillation and clustering.
  • To enable identification of text clusters from application-specific reference images.
  • To improve the localization and coverage of regions of interest (ROIs).

Main Methods:

  • Distillation of local image features combined with clustering analysis.
  • Application-specific reference images for feature learning and extraction.
  • Iterative adjustment for region expansion to ensure complete text coverage.

Main Results:

  • Successfully identified text clusters despite sparsity of feature points.
  • Localized structured regions by identifying high feature density clusters.
  • Demonstrated effectiveness in text region detection for invoices and banknotes.

Conclusions:

  • The proposed technique offers an effective approach for structured region detection.
  • The method shows promise for machine vision applications requiring precise information extraction.
  • This technique advances the field of image analysis for document processing.