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Few-Shot Pixel-Precise Document Layout Segmentation via Dynamic Instance Generation and Local Thresholding.

Axel De Nardin¹, Silvia Zottin¹, Claudio Piciarelli¹

¹Department of Mathematics, Computer Science and Physics, Università degli Studi di Udine, Via delle Scienze 206, 33100 Udine, Italy.

International Journal of Neural Systems

|August 11, 2023

Summary

This summary is machine-generated.

This study introduces a novel few-shot learning framework for document layout segmentation in cultural heritage studies. The AI approach effectively analyzes handwritten texts with minimal data, matching state-of-the-art performance.

Keywords:

Document layout segmentation few-shot learning handwritten documents analysis image segmentation layout analysis

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Area of Science:

Digital Humanities
Computer Vision
Artificial Intelligence

Background:

The study of cultural heritage increasingly requires AI tools.
Document layout segmentation is crucial for analyzing historical documents, especially handwritten ones.
Current methods demand extensive labeled data, which is often unavailable due to time and expertise constraints.

Purpose of the Study:

To develop an effective few-shot learning framework for document layout segmentation.
To address the data scarcity challenge in analyzing cultural heritage documents.

Main Methods:

Proposed a novel framework for document layout segmentation.
Introduced two key components: dynamic instance generation and a segmentation refinement module.
Employed a few-shot learning approach to minimize data requirements.

Main Results:

Achieved performance comparable to state-of-the-art methods.
Demonstrated effectiveness on the Diva-HisDB dataset.
Required only a fraction of the typical training data.

Conclusions:

The proposed few-shot learning framework offers a viable solution for document layout segmentation in cultural heritage.
This approach significantly reduces the need for large annotated datasets.
Enables more accessible AI-driven analysis of historical handwritten documents.