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Published on: July 22, 2025
Stochastic language models for style-directed layout analysis of document images
1IBM Almaden Res. Center, San Jose, CA 95120, USA. kanungo@almaden.ibm.com
Summary
This study introduces a novel image segmentation algorithm for document analysis. It allows users to define physical document styles, improving segmentation accuracy for complex layouts like bilingual dictionaries.
Area of Science:
- Computer Vision
- Document Image Analysis
- Machine Learning
Background:
- Image segmentation is crucial for document image analysis.
- Existing algorithms often lack user-defined style incorporation.
- Document structure modeling is complex and requires flexible approaches.
Purpose of the Study:
- To develop a user-guided image segmentation algorithm for document analysis.
- To incorporate user-specified physical document styles into the segmentation process.
- To model document structure using hierarchical stochastic regular grammars.
Main Methods:
- Developed a segmentation algorithm modeling document structure hierarchically.
- Utilized stochastic regular grammars to describe document regions.
- Enabled user specification of hierarchy and language, with probability estimation from groundtruth data.
Main Results:
- Successfully demonstrated the segmentation algorithm on bilingual dictionary images.
- The algorithm effectively segments documents based on user-defined physical styles.
- Validated the model's ability to handle complex document layouts.
Conclusions:
- The proposed algorithm offers a flexible and user-centric approach to document image segmentation.
- Incorporating user-defined styles enhances segmentation performance for specialized document types.
- This method advances document analysis by integrating structural and stylistic information.