Synthetic Scientific Document Layout Graph Dataset

Synthetic Scientific Document Layout Graph Dataset provides graph-structured representations for analyzing document layouts and multimodal reasoning. The dataset's exact size, creator, and publication date are not specified in the provided information.

Use Cases

• Train graph neural networks to predict relationships between layout elements like text blocks, figures, and tables.
• Develop multimodal models that jointly reason over visual layout features and textual content for document classification.
• Benchmark models for tasks such as document element detection and spatial relationship inference using graph connectivity features.
• Generate synthetic training data for layout parsing algorithms to improve generalization on real-world scientific documents.

Strengths

• Graph-structured format explicitly designed for representing complex document layouts.
• Focus on scientific documents provides a domain-specific context for multimodal reasoning.

Limitations

• Dataset size, row count, and sample diversity are unknown, limiting assessment of statistical power.
• Synthetic nature may introduce a distribution gap compared to real-world document layouts.

Provenance

Collection Method

Synthetically generated for document layout and multimodal reasoning research.