olmocr vs Unlimited-OCR
Open toolkit that linearizes messy PDFs — scans, tables, equations, handwriting — into clean ordered Markdown with a self-hosted vision-language model. Built for LLM training data and RAG ingestion. — versus — Baidu's open OCR VLM that parses entire multi-page documents in one shot — 'unlimited' long-horizon parsing pushing DeepSeek-OCR further. MIT weights on HF; serve via transformers, vLLM or SGLang.
Same job — self-hosted VLM that linearizes messy PDFs into LLM-ready text: olmocr is AllenAI's page-pipeline toolkit built for training-data ingestion; Unlimited-OCR bets on one-shot long-horizon parsing that preserves cross-page structure.
| olmocr | Unlimited-OCR | |
|---|---|---|
| Stars | 19k | 14k |
| Forks | 1.6k | 1.2k |
| Language | Python | Python |
| License | Apache-2.0 | MIT |
| Last activity | 3 months ago | 14 days ago |
| Topics | rag, ocr | ocr, rag |
| Curated connections | 8 | 5 |
olmocr — the curator's take
Reach for olmocr when you have lots of messy PDFs to convert into high-quality text for a training corpus or RAG index and you have GPU to run the VLM. It is the parsing FRONT-END of a pipeline, not a RAG system itself — pair it with an indexer/retriever (LlamaIndex) and a vector store (Chroma). NOT worth it for clean, digital-native PDFs where a cheap text extractor does the job — running a vision-language model for those is overkill.
Unlimited-OCR — the curator's take
The current bar if you batch-parse long PDFs: one-shot multi-page parsing keeps cross-page structure (tables spanning pages, running sections) that page-by-page OCR pipelines lose, and first-party vLLM/SGLang recipes plus official Docker images make serving unusually painless for a fresh research model. NOT a lightweight dependency — it's a GPU-hungry VLM; for occasional single pages classic OCR or a hosted API is cheaper. Inference-only repo: no training code, and evaluation beyond the paper's claims is on you.