opendatalab/PDF-Extract-Kit
A Comprehensive Toolkit for High-Quality PDF Content Extraction
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
No releases in the last 90 days; the most recent was 755 days ago, with 10 contributors on GitHub.
Use Cases & Benefits
- Provides a modular Python toolkit for extracting high-quality content from complex PDF documents using state-of-the-art models.
- Enables robust, accurate parsing across diverse document types by integrating fine-tuned models for layout, formula, OCR, and table recognition.
- Use for developing custom PDF content extraction applications like document translation or Q&A assistants with flexible model combinations.
- Use for extracting and converting table images into LaTeX, HTML, or Markdown source code for academic or financial report processing.
- Use for detecting and recognizing complex mathematical formulas in research papers or educational materials with advanced formula models.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
AGPL 3.0
Network copyleft: like the GPL, and also triggered by offering the software over a network.
Community files
- README: present
Watch-outs
- AGPL-style copyleft also applies when the software is offered over a network.
- Warning: No commits in over a year (last push Jan 2025).
General guidance from repository metadata, not legal advice. How we score
From the README
PDF-Extract-Kitis a powerful open-source toolkit designed to efficiently extract high-quality content from complex and diverse PDF documents. Here are its main features and advantages:Experience PDF-Extract-Kit now and unlock the limitless potential of PDF documents!
Note: PDF-Extract-Kit is designed for high-quality document processing and functions as a model toolbox. If you are interested in extracting high-quality document content (e.g., converting PDFs to Markdown), please use MinerU, which combines the high-quality predictions from PDF-Extract-Kit with specialized engineering optimizations for more convenient and efficient content extraction. If you're a developer looking to create engaging applications such as document translation, document Q&A, or document assistants, you'll find it very convenient to build your own projects using PDF-Extract-Kit. In particular, we will periodically update the PDF-Extract-Kit/project directory with interesting applications, so stay tuned!
We welcome researchers and engineers from the community to contribute outstanding models and innovative applications by submitting PRs to become contributors to the PDF-Extract-Kit project.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
748-6
Open issues
107+1
Compared with peers in Backend
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| anthropics/skills | 192.3K | +5.2K | 75 | — |
| zed-industries/zed | 94.7K | +2.3K | 75 | 6 days ago |
| google/skills | 23.8K | +2.0K | 75 | — |
| NationalSecurityAgency/ghidra | 99.4K | +1.8K | 75 | 19 days ago |
| DuarteSantos8/openGym | 20.8K | +1.7K | 75 | 7 days ago |
| opendatalab/PDF-Extract-Kit | 10.1K | — | 11 | 755 days ago |
| Category median | 94.7K | +2.0K | 75 | — |
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