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Published on: June 23, 2023
Skeletonization for fuzzy degraded character images.
Summary
This study introduces a novel topography-based skeletonization method for fuzzy or grayscale images, preserving information and reducing deformation. The approach accurately extracts skeletons, even for merged characters, by identifying saddle points.
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- Traditional skeletonization algorithms often rely on binary images, leading to information loss and distortion.
- Existing methods struggle with fuzzy or grayscale images, limiting their applicability in real-world scenarios.
Purpose of the Study:
- To develop a topography-based skeletonization approach for direct application on fuzzy or grayscale images.
- To minimize information loss and deformation during the skeletonization process.
- To accurately extract skeletons and handle merged characters effectively.
Main Methods:
- Utilizing a membership function to determine the degree of membership for ridge points.
- Linking significant ridge points to form skeleton strokes.
- Employing saddle point detection and multiple context confirmation for merged character segmentation.
Main Results:
- The proposed algorithm successfully reduces deformation at junction points.
- Accurate extraction of the complete skeleton is achieved, even when characters are broken.
- Reliable identification of breaking positions for merged characters using saddle points.
Conclusions:
- The topography-based approach offers a robust solution for skeletonization on non-binary images.
- The method effectively preserves image information and reduces structural distortions.
- The algorithm demonstrates improved performance in segmenting complex or merged characters.
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