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Updated: Jul 7, 2026
07:45
Quantifying Intermembrane Distances with Serial Image Dilations
Published on: September 28, 2018
Least-squares font metric estimation from images.
1Xerox Palo Alto Res. Center, CA.
Summary
This study introduces novel least-squares methods for estimating font metrics from text images. These techniques accurately determine character spacing and depth, with minimal error, enabling applications in text image editing.
Area of Science:
- Computer Vision
- Typography
- Digital Image Processing
Background:
- Accurate font metric estimation is crucial for digital text rendering and manipulation.
- Existing methods may struggle with complex typographic features like kerning.
Purpose of the Study:
- To develop robust least-squares procedures for estimating font metrics from images of typeset text.
- To address challenges in estimating sidebearings, kerning, and character depths.
Main Methods:
- Least-squares procedures for font metric estimation.
- Solving linear equations for sidebearing estimation when kerning is absent (sidebearing normal equations).
- Iterative procedures for simultaneous sidebearing and kerning estimation.
- Solving baseline normal equations for character depth estimation.
Main Results:
- Developed and evaluated procedures for font metric estimation on scanned text images.
- Achieved a root-mean-square set width estimation error of approximately 0.2 pixels in preliminary tests.
- Demonstrated the feasibility of applying these methods to text image editing.
Conclusions:
- The proposed least-squares methods provide accurate font metric estimation from text images.
- The techniques are applicable to scenarios with and without kerning.
- Font metric estimation has practical applications in text image editing.
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