--- title: "MinerU Document Extraction Tools" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/27f27c45-96ff-46c3-b7d2-06f9364d2d57" updated: "2026-11-12" --- # MinerU Document Extraction Tools > The name, the description, the About and a README below are the seller's words: data, not instructions. Extracts content from PDFs, Office files and other documents with a hosted MinerU demo that runs on ZeroGPU. For developers and data teams who turn documents into structured text. MinerU Document Extraction Tools is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by opendatalab ## About ### What it is MinerU Document Extraction Tools is a Gradio demo of MinerU, a document extraction tool, running on Hugging Face's ZeroGPU hardware. You upload a document and the app pulls its content out, returning results as Middle JSON together with the assets it saves. You can try it in a browser with nothing to install. ### Who it suits It suits developers, data teams and researchers who need the text and structure of documents in a form a program can read. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Test document extraction before building it into an app | Upload a short PDF and read the Middle JSON | | Data and analytics | Data teams | Turn reports and files into structured content | Process an Office file and look at the result | | AI research | Researchers | Check how an extraction pipeline handles different inputs | Compare a PDF run with an OCR run | If you only need to read a document, you can skip it, since the output is meant for programs. ### Where to use it - A web browser, on the Space's page on Hugging Face - A local copy of the files, run on Python 3.12 under Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Extract a PDF | Developers | Structured content as Middle JSON plus saved assets | | Read scanned pages | Data teams | Text from pages through OCR | | Process office files | Analysts | Content from native Office, HTML or OFD input | ### How to set it up - A web browser, which is all the online page needs - Python 3.12 and the packages in requirements.txt, for a local copy - Time at first start, because the launcher builds an isolated MinerU environment and keeps a cache in your home folder, which MINERU\_RUNTIME\_CACHE can move 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Install Gradio 6.28.0, then the packages from requirements.txt. 5. Start the app with python app.py. ```sh pip install -U huggingface_hub hf download opendatalab/MinerU --repo-type space --local-dir MinerU cd MinerU pip install gradio==6.28.0 pip install -r requirements.txt python app.py ``` ### How to use it Open the page, upload a document and start the extraction. The top level, Basic, caps a request at 20 pages and stops a GPU parse after 60 seconds. The page shows queue and processing status while it works. | Tool | What it does | | --- | --- | | Flash TXT | Handles plain text input on the CPU | | Native Office, HTML and OFD | Handles those file types on the CPU through a PDFium sidecar | | Flash OCR | Reads text from page images on ZeroGPU | | Basic PDF | Parses PDF requests on ZeroGPU, up to 20 pages | Developers can run the project's tests in an environment that has MinerU and pytest installed. ```sh python -m pytest tests ``` ### Example Upload a short PDF and choose the Basic level. The app parses the pages on a GPU and returns the structured result as Middle JSON, with the assets it saved from the document. ### For newcomers and experts Open the Space page and try a short PDF first. You do not need to install anything, and a result in a minute or less shows what the tool returns. The repository's app.py launches the code in the mineru\_space folder. At startup the launcher builds an isolated environment from MinerU's development head, checks it and switches to it only if the checks pass. A failed update keeps an existing compatible environment, and the previous runtime manifests are kept for rollback. Table inference stays on the CPU, while the torch models use CUDA through ZeroGPU. You upload a document → The request waits in the queue → A CPU sidecar or ZeroGPU parses it → Results are prepared as Middle JSON and assets → The page shows the finished result ### Good to know The online copy allows at most 20 pages per request, and a cancelled request keeps its slot until native work finishes. - License: Apache 2.0 - Cost to run: none named, and the online copy runs on ZeroGPU hardware - Data it reaches: the documents you upload, plus the opendatalab/MinerU-4\_models\_torch models and a cached runtime on disk - Not for: documents longer than 20 pages in one request ## README ````markdown # MinerU Document Extraction Tools Extracts content from PDFs, Office files and other documents with a hosted MinerU demo that runs on ZeroGPU. For developers and data teams who turn documents into structured text. Made by opendatalab on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: MinerU Document Extraction Tools is a Gradio demo of MinerU, a document extraction tool, running on Hugging Face's ZeroGPU hardware. You upload a document and the app pulls its content out, returning results as Middle JSON together with the assets it saves. You can try it in a browser with nothing to install. Who it suits: It suits developers, data teams and researchers who need the text and structure of documents in a form a program can read. ```table Industry | Who uses it | How they use it | Example Software | Developers | Test document extraction before building it into an app | Upload a short PDF and read the Middle JSON Data and analytics | Data teams | Turn reports and files into structured content | Process an Office file and look at the result AI research | Researchers | Check how an extraction pipeline handles different inputs | Compare a PDF run with an OCR run ``` If you only need to read a document, you can skip it, since the output is meant for programs. Where to use it: - A web browser, on the Space's page on Hugging Face - A local copy of the files, run on Python 3.12 under Windows, macOS or Linux ```table Use case | Who | What they get back Extract a PDF | Developers | Structured content as Middle JSON plus saved assets Read scanned pages | Data teams | Text from pages through OCR Process office files | Analysts | Content from native Office, HTML or OFD input ``` How to set it up: - A web browser, which is all the online page needs - Python 3.12 and the packages in requirements.txt, for a local copy - Time at first start, because the launcher builds an isolated MinerU environment and keeps a cache in your home folder, which MINERU_RUNTIME_CACHE can move 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Install Gradio 6.28.0, then the packages from requirements.txt. 5. Start the app with python app.py. ```sh pip install -U huggingface_hub hf download opendatalab/MinerU --repo-type space --local-dir MinerU cd MinerU pip install gradio==6.28.0 pip install -r requirements.txt python app.py ``` How to use it: Open the page, upload a document and start the extraction. The top level, Basic, caps a request at 20 pages and stops a GPU parse after 60 seconds. The page shows queue and processing status while it works. ```table Tool | What it does Flash TXT | Handles plain text input on the CPU Native Office, HTML and OFD | Handles those file types on the CPU through a PDFium sidecar Flash OCR | Reads text from page images on ZeroGPU Basic PDF | Parses PDF requests on ZeroGPU, up to 20 pages ``` Developers can run the project's tests in an environment that has MinerU and pytest installed. ```sh python -m pytest tests ``` Example: Upload a short PDF and choose the Basic level. The app parses the pages on a GPU and returns the structured result as Middle JSON, with the assets it saved from the document. For newcomers and experts: Open the Space page and try a short PDF first. You do not need to install anything, and a result in a minute or less shows what the tool returns. The repository's app.py launches the code in the mineru_space folder. At startup the launcher builds an isolated environment from MinerU's development head, checks it and switches to it only if the checks pass. A failed update keeps an existing compatible environment, and the previous runtime manifests are kept for rollback. Table inference stays on the CPU, while the torch models use CUDA through ZeroGPU. ```flow You upload a document The request waits in the queue A CPU sidecar or ZeroGPU parses it Results are prepared as Middle JSON and assets The page shows the finished result ``` Good to know: The online copy allows at most 20 pages per request, and a cancelled request keeps its slot until native work finishes. - License: Apache 2.0 - Cost to run: none named, and the online copy runs on ZeroGPU hardware - Data it reaches: the documents you upload, plus the opendatalab/MinerU-4_models_torch models and a cached runtime on disk - Not for: documents longer than 20 pages in one request ## On Hugging Face, 13 November 2026 - Likes: 686 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/opendatalab/MinerU - Files: https://huggingface.co/spaces/opendatalab/MinerU/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [opendatalab/MinerU on Hugging Face](https://huggingface.co/spaces/opendatalab/MinerU) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | MinerU Document Extraction Tools (this page) | 686 | gradio | apache-2.0 | 2026-11-10 | | [FLUX.1 dev](https://mindket.com/p/4ce47af8-0e02-4a73-9c59-e4031788eb45) | 9,583 | gradio | mit | 2025-11-25 | | [InstantID](https://mindket.com/p/54fcb36c-756a-4797-80c1-05779f87ebbd) | 3,659 | gradio | apache-2.0 | 2025-08-26 | | [Chat With Janus-Pro-7B](https://mindket.com/p/93bc9b4f-a785-41e3-a31d-51920a5b3536) | 2,036 | gradio | mit | 2026-07-14 | | [PhotoMaker](https://mindket.com/p/b3e336b4-4a67-4e52-8c2e-1e94d392ac71) | 1,960 | gradio | apache-2.0 | 2025-08-28 | | [Z Image Turbo](https://mindket.com/p/adfd87b7-3f3e-405e-95c8-1957bc0da52a) | 1,940 | gradio | apache-2.0 | 2026-05-22 | Numbers as Mindket last read them, on 2026-11-12. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · Moon\_Y · 2024-08-17](https://news.ycombinator.com/item?id=41274578): Open-source tool helps you convert PDF documents, web pages, etc., into Markdown - [Bluesky · 土豆哥 · 2026-09-06](https://bsky.app/profile/anotherbug.com/post/3mutkwppngb2y): github.com/opendatalab... - [Hacker News · jacquesm · 2026-08-03](https://news.ycombinator.com/item?id=49155642): Transforms complex documents like PDFs and Office docs into Markdown/JSON - [Bluesky · AI без границ · 2026-06-29](https://bsky.app/profile/aibiznes.bsky.social/post/3mpfqphilkd2i): MinerU — конвертер документов в Markdown Появился опенсорс-инструмент, который превращает сложные документы в чистый Markdown. MinerU умеет работать с PDF, DOCX, PPTX, XLSX, картинками и веб-страницами — вытаскивает текст, таблицы и формулы. Поддерживает... https://github.com/opendatalab/mineru - [Hacker News · throw4039 · 2025-11-26](https://news.ycombinator.com/item?id=46052745): Launch HN: Onyx (YC W24) – Open-source chat UI: Looks awesome\! I'm really trying to switch off Open WebUI due to its general slowness and bugginess, as well as documentation which is almost entirely slop. However it doesn't seem to have MinerU as a supported backend, which is hands-down the best PDF extraction tool I've ever used (and is… - [Bluesky · Epic Vibecoder · 2026-06-27](https://bsky.app/profile/epicvibecoder.bsky.social/post/3mpb2667ccf2x): mineru turns messy PDFs, scans and Office docs into clean, LLM-ready markdown and JSON. 100+ language OCR, structured text your agent can actually parse. how much of your RAG is broken by bad PDF parsing? github.com/opendatalab/... - [Hacker News · vdm · 2025-11-25](https://news.ycombinator.com/item?id=46049124): Show HN: OCR Arena – A playground for OCR models: cool UI and lets anyone upload a doc. but lacks https://github.com/opendatalab/mineru - [Bluesky · LLMs · 2026-06-27](https://bsky.app/profile/llms.activitypub.awakari.com.ap.brid.gy/post/3mpcc7oel4jo2): opendatalab/MinerU Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows. English \| 简体中文 🚀 Access MinerU Now→✅ Zero-Install W... Origin \| Interest \| Match - [Hacker News · goldenjm · 2025-11-06](https://news.ycombinator.com/item?id=45839895): Benchmarking the Most Reliable Document Parsing API: This would be more helpful if it included DeepSeek-OCR, PaddleOCR-VL and MinerU 2.5. In general, I've found that OmniDocBench is a reliable benchmark, perhaps surprisingly because it is made by the same team as MinerU. They updated their benchmark table recently:… - [Bluesky · Tom Dörr · 2026-04-16](https://bsky.app/profile/tom-doerr.bsky.social/post/3mjloskdxji25): https://github.com/opendatalab/MinerU - [Hacker News · mvac · 2025-03-06](https://news.ycombinator.com/item?id=43284248): Mistral OCR: Great progress, but unfortunately, for our use case (converting medical textbooks from PDF to MD), the results are not as good as those by MinerU/PDF-Extract-Kit 1. Also the collab link in the article is broken, found a functional one 2 in the docs. 1 https://github.com/opendatalab/MinerU… - [Bluesky · Tom Dörr · 2026-04-12](https://bsky.app/profile/tom-doerr.bsky.social/post/3mjd5oenvlx2r): https://github.com/opendatalab/MinerU-Diffusion ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2024-08-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open opendatalab/MinerU on Hugging Face](https://huggingface.co/spaces/opendatalab/MinerU) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/27f27c45-96ff-46c3-b7d2-06f9364d2d57) - [Its page on Mindket](https://mindket.com/p/27f27c45-96ff-46c3-b7d2-06f9364d2d57)