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SOTA multimodal document parsing with 1.2B parameters outperforming GPT-4o, converts PDFs to LLM-ready Markdown/JSON

Open-source PDF parser for AI-ready data, converting PDFs into Markdown/JSON/HTML/Tagged PDF with layout analysis and reading-order detection; ranks #1 overall on extraction benchmarks with deterministic bounding boxes and hybrid AI mode (26K+ stars, Apache 2.0)

Machine learning software for extracting structured metadata from scholarly documents

Production-grade ETL for transforming complex documents into structured formats, with open-source API

Advanced OCR with PP-StructureV3 document parsing, 13% accuracy improvement, supports 80+ languages

High-accuracy PDF→Markdown/JSON/HTML conversion, specialized for tables/formulas/code blocks with benchmark scripts

State-of-the-art OCR model converting images and PDFs into structured HTML/Markdown/JSON with full layout preservation, excelling at complex tables, handwritten notes, mathematical equations, forms, and 90+ languages; tops external olmOCR benchmarks and ships a pip-installable `chandra-ocr` package with HuggingFace local and vLLM server inference modes (datalab.to, the team behind Marker, 12.5K+ stars, Apache 2.0 code / OpenRAIL-M model license)

Diffusion-based document OCR framework replacing autoregressive decoding with block-level parallel diffusion decoding, enabling high-accuracy text recognition in scientific PDFs (613+ stars, MIT License)

Toolkit for linearizing academic PDFs into LLM-ready text with high accuracy and structure preservation, optimized for scientific literature extraction

Neural optical understanding for academic documents, transforms scientific PDFs to Markdown with mathematical formula support

Comprehensive toolkit for high-quality PDF content extraction with layout detection, formula recognition, and OCR

Parse scientific papers to structured fields (title/author/sections/references)

Large-scale PDF/LaTeX/JATS parsing to standardized JSON for millions of papers