olmOCR vs PaddleOCR
Short answer
olmOCR: Ai2's vision-language OCR that turns PDFs and scans into clean Markdown, including tables, equations and handwriting. PaddleOCR: OCR toolkit and document AI engine that turns scans, images and PDFs into LLM-ready text, Markdown or JSON. Both are in our Document processing category.
Choose PaddleOCR if you want: no GPU server needed, an actively maintained project (olmOCR is stale) and larger community (92.2k vs 19.8k GitHub stars).
PaddleOCR publishes no official RAM figure, so we cannot compare hosting cost directly.
Last updated · computed from each project's official docs and live GitHub data · sources
olmOCR vs PaddleOCR side by side
| olmOCR | PaddleOCR | |
|---|---|---|
| Category | Document processing | Document processing |
| What it is | Ai2's vision-language OCR that turns PDFs and scans into clean Markdown, including tables, equations and handwriting. | OCR toolkit and document AI engine that turns scans, images and PDFs into LLM-ready text, Markdown or JSON. |
| License | Apache-2.0 | Apache-2.0 |
| License type | Permissive | Permissive |
| License conditions | Permissive. Internal and commercial use, modification and redistribution allowed; keep the license and notices. | Permissive. Internal and commercial use allowed; keep license and notices. |
| Status | Stale (no commit in 180+ days) | Active |
| GitHub stars | 19.8k | 92.2k |
| Last commit | Mar 25, 2026 | Sep 16, 2026 |
| Latest release | v0.4.27 | v3.7.0 |
| Minimum hardware | 30 GB disk | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | Not tested yet | Not tested yet |
| GPU | Required for local inference: a recent NVIDIA GPU with at least 12 GB of GPU RAM (tested on RTX 4090, L40S, A100, H100). Without a GPU you can point it at a remote vLLM or OpenAI-compatible server. | Optional. The server uses a GPU by default if available, otherwise the CPU. |
| Est. server cost | GPU server (not estimated) | Not estimated |
| Deploy method | Docker (GPU image with model included) | pip + PaddleX serving (HTTP API) |
| Known gotchas | 3 listed | 4 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Not documented | Not documented |
| SSO (OIDC / SAML / LDAP) | Not documented | Not documented |
| Audit log | Not documented | Not documented |
| RBAC | Not documented | Not documented |
| Details | Full olmOCR guide → | Full PaddleOCR guide → |
Key differences
- GPU: olmOCR: needed for practical use. PaddleOCR: optional / see notes.
- Est. server cost: olmOCR: GPU server (not estimated). PaddleOCR: not estimated (no official RAM figure).
- Deploy: olmOCR: Docker (GPU image with model included) · single container. PaddleOCR: pip + PaddleX serving (HTTP API) · pip + PaddleX serving (HTTP API).
- Status: olmOCR: Stale (no commit in 180+ days). PaddleOCR: Active.
- GitHub stars: olmOCR: 19.8k. PaddleOCR: 92.2k.
- Last commit: olmOCR: Mar 25, 2026. PaddleOCR: Sep 16, 2026.
- Latest release: olmOCR: v0.4.27 (Mar 12, 2026). PaddleOCR: v3.7.0 (Jun 11, 2026).
Same for both: Category: Document processing; License: Apache-2.0 (Permissive); Official hardware: not published.
olmOCR vs PaddleOCR: FAQ
olmOCR or PaddleOCR: which should a small business choose?
Choose PaddleOCR if you want: no GPU server needed, an actively maintained project (olmOCR is stale) and larger community (92.2k vs 19.8k GitHub stars). PaddleOCR publishes no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, olmOCR or PaddleOCR?
olmOCR: GPU server (not estimated). PaddleOCR: not estimated (no official RAM figure). Our estimate is the cheapest Hetzner Cloud plan that meets each project's official RAM and vCPU figure; it covers the server only.
How much hardware do olmOCR and PaddleOCR need?
olmOCR: not published. PaddleOCR: not published. olmOCR needs a GPU for practical use.
What is the license difference between olmOCR and PaddleOCR?
olmOCR: Apache-2.0 (Permissive). Permissive. Internal and commercial use, modification and redistribution allowed; keep the license and notices. PaddleOCR: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed; keep license and notices. This is a summary, not legal advice.
Is olmOCR or PaddleOCR more actively maintained?
olmOCR: Stale (no commit in 180+ days), last commit Mar 25, 2026. PaddleOCR: Active, last commit Sep 16, 2026. GitHub data refreshed Nov 3, 2026.
Which is easier to deploy, olmOCR or PaddleOCR?
olmOCR installs via Docker (GPU image with model included) (single container); PaddleOCR via pip + PaddleX serving (HTTP API) (pip + PaddleX serving (HTTP API)). olmOCR lists 3 known gotchas and PaddleOCR lists 4; see each app page.
Related
- olmOCR self-hosting guide · PaddleOCR self-hosting guide
- All olmOCR alternatives, ranked
- All PaddleOCR alternatives, ranked
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- Docling Serve vs PaddleOCR
- MinerU vs olmOCR
- MinerU vs PaddleOCR
- OCRmyPDF vs olmOCR
- OCRmyPDF vs PaddleOCR
- olmOCR vs Paperless-AI
- olmOCR vs paperless-gpt
- olmOCR vs Paperless-ngx
- olmOCR vs Unstract
- PaddleOCR vs Paperless-AI
- PaddleOCR vs paperless-gpt
- Add a third app in the interactive comparison
Sources
- olmOCR GitHub repository: github.com/allenai/olmocr
- olmOCR documentation: github.com/allenai/olmocr#readme
- olmOCR license: github.com/allenai/olmocr/blob/main/LICENSE
- olmOCR hardware requirements: github.com/allenai/olmocr#python-installation
- olmOCR deploy instructions: github.com/allenai/olmocr#using-docker
- PaddleOCR GitHub repository: github.com/PaddlePaddle/PaddleOCR
- PaddleOCR documentation: www.paddleocr.ai/
- PaddleOCR license: github.com/PaddlePaddle/PaddleOCR/blob/main/LICENSE
- PaddleOCR hardware requirements, PaddleOCR deploy instructions: www.paddleocr.ai/latest/en/version3.x/inference_deployment/serving/serving.html
- Hetzner Cloud pricing used for cost estimates (read 2026-11-01): www.hetzner.com/cloud/regular-performance
- GitHub REST API (stars, commits, releases): docs.github.com/en/rest
Every value is generated from the same verified data as the app pages. Spotted something wrong? Tell us.