[jlcases]

This is a valuable contribution. The quality of ML models heavily depends on the quality of training data, and extracting structured information from unstructured documents (like PDFs) is a critical bottleneck.

A key challenge after OCR is organizing the extracted data into a coherent knowledge structure. We've seen significant improvements in downstream ML tasks when the extracted data is organized using a hierarchical, MECE (Mutually Exclusive, Collectively Exhaustive) framework. This ensures that relationships between entities (tables, diagrams, text) are explicitly captured.

Does your pipeline include capabilities for semantic structuring of the extracted content beyond basic layout analysis? That seems like the next frontier for maximizing the value of OCR data in ML training.

[ses425500000]

Thanks for the insightful comment! You’re absolutely right — organizing extracted data into a coherent, semantically meaningful structure is critical for high-quality ML training.

Right now, the pipeline focuses on generating OCR outputs optimized for ML models by cleaning, deduplicating, and segmenting content across modalities (text, tables, figures, formulas). For diagrams and tables, we add semantic tags and preserve layout relationships to aid downstream modeling.

I’m planning to add a semantic structuring module that goes beyond basic layout analysis — something that builds hierarchical, MECE-style representations and identifies entity relationships across sections. That’s absolutely the next frontier, and I really appreciate you pointing it out.

Thanks again for the thoughtful feedback!

[cAtte_]

why are you using an LLM to reply to every comment?