olmocr vs TurboOCR
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 — GPU document parser in C++/TensorRT: PP-OCRv6 OCR, layout, tables and formulas to Markdown at 200-559 img/s on one GPU — no VLM, HTTP + gRPC, one-line Docker deploy.
olmOCR linearizes messy PDFs with a self-hosted VLM; TurboOCR refuses the VLM entirely and wins ~20x on throughput. Pick by corpus: pristine forms → TurboOCR, chaotic scans → VLM.
| olmocr | TurboOCR | |
|---|---|---|
| Stars | 20k | 1.1k |
| Forks | 1.6k | 110 |
| Language | Python | C++ |
| License | Apache-2.0 | MIT |
| Last activity | 5 months ago | 11 days ago |
| Topics | rag, ocr | ocr |
| Curated connections | 12 | 6 |
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.
TurboOCR — the curator's take
The throughput bet: a classic multi-stage pipeline on TensorRT instead of a VLM — ~20 pages/s structured parsing where VLM parsers run ~1. Sharp on forms and receipts. When NOT: Linux + NVIDIA Turing+ only (Metal/ROCm still cooking), first start compiles engines (up to an hour on older cards), and benchmarks are self-published on its own harness — for messy scans and handwriting a VLM parser may still out-read it.