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Script-independent text line segmentation in freestyle handwritten documents.

Yi Li1, Yefeng Zheng, David Doermann

  • 1Language and Media Processing Laboratory, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA. liyi@cfar.umd.edu

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Summary
This summary is machine-generated.

This study introduces a new method for segmenting text lines in handwritten documents using density estimation and level set methods. The approach accurately separates text lines, outperforming existing techniques across various scripts.

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Area of Science:

  • Document Analysis
  • Image Processing
  • Computer Vision

Background:

  • Text line segmentation in freestyle handwritten documents is a challenging problem.
  • Curvilinear text lines and small inter-line gaps hinder traditional algorithms.
  • Existing methods often rely on script-specific knowledge.

Purpose of the Study:

  • To propose a novel, script-independent approach for text line segmentation in handwritten documents.
  • To improve the accuracy and robustness of text line segmentation.
  • To address challenges posed by curvilinear text and small gaps.

Main Methods:

  • Density estimation to create a probability map of text line pixels.
  • Level set method for evolving an initial estimate to delineate text line boundaries.
  • Script-independent processing, avoiding reliance on specific writing systems.

Main Results:

  • The proposed algorithm consistently outperforms previous methods on diverse handwritten scripts (Arabic, Chinese, Korean, Hindi).
  • Demonstrated robustness to scale changes, rotation, and noise.
  • Achieved superior performance compared to connected component-based methods.

Conclusions:

  • The novel density estimation and level set approach offers a robust and effective solution for handwritten text line segmentation.
  • The script-independent nature makes it broadly applicable across various languages.
  • This method advances the field of document analysis for challenging handwritten materials.