doctr vs olmocr
docTR: two-stage OCR in PyTorch — detect words, then recognize them — with pretrained detection and recognition architectures you can mix, plus layout detection and rotated-page handling. — versus — 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.
Both turn messy pages into text for downstream AI. olmOCR linearizes whole documents into ordered Markdown with a self-hosted VLM, aimed at training data and RAG ingestion; docTR stops at accurate word-level recognition you can build any layout logic on.
| doctr | olmocr | |
|---|---|---|
| Stars | 6.3k | 19k |
| Forks | 672 | 1.6k |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 3 days ago | 5 months ago |
| Topics | ocr | rag, ocr |
| Curated connections | 3 | 11 |
doctr — the curator's take
The dependable choice when you want OCR as a library rather than a model to serve: pick a detector and a recognizer, call `ocr_predictor(pretrained=True)`, get word-level boxes and text from PDFs or images, with rotated-page handling and optional layout regions. It predates the VLM wave and that's the point — small models, no prompt, deterministic output, trainable on your own data, and cheap enough to run per-page at volume. Two caveats: it gives you words and boxes, not document structure, so reading order, tables and markdown are your job (pair it with a parser); and stewardship moved from Mindee to t2k GmbH, so check commit cadence against the newer VLM OCR crowd before standardizing on it.
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.