olmocr vs text-extract-api
Side-by-side comparison of two AI agent tools
Short answer
- olmocr is growing faster: +413 GitHub stars in the last 30 days vs +17 for text-extract-api.
- Pick olmocr for: toolkit for linearizing PDFs for LLM datasets/training. Pick text-extract-api for: local FastAPI for OCR extraction and PII removal from images, PDFs and Office files to Markdown or JSON.
From GitHub data refreshed daily.
olmocropen-source
Toolkit for linearizing PDFs for LLM datasets/training
text-extract-apiopen-source
Local FastAPI for OCR extraction and PII removal from images, PDFs and Office files to Markdown or JSON
Metrics
| olmocr | text-extract-api | |
|---|---|---|
| Stars | 19.7k | 3.2k |
| Star velocity /mo | 413.3684210526315 | 16.736842105263158 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Downloads (30d, npm + PyPI) | 17.4K | — |
| Overall score | 0.3454145003701764 | 0.20484123380037875 |
Pros
- +Excellent handling of complex document layouts including equations, tables, handwriting, and multi-column formats with natural reading order preservation
- +Cost-effective processing at under $200 per million pages, making it economical for large-scale dataset creation
- +Continuous model improvements with recent releases showing significant performance gains and reduced hallucinations on blank documents
- +完全本地化处理，无外部依赖，确保数据隐私和安全性
- +支持多种先进OCR策略（LLaMA Vision、EasyOCR等），识别精度极高
- +集成分布式队列和缓存机制，支持大规模文档批量处理
Cons
- -Requires GPU resources due to 7B parameter model, making it computationally intensive and potentially expensive to run
- -May require multiple retries for some documents to achieve optimal results
- -Limited to image-based document formats (PDF, PNG, JPEG) and requires technical expertise for setup and optimization
- -需要安装多个依赖组件（Docker、Ollama），初始设置较为复杂
- -本地运行PyTorch模型需要较大计算资源和存储空间
Use Cases
- •Converting academic papers and research documents with complex equations and figures for LLM training datasets
- •Processing legacy document archives with multi-column layouts and mixed content types into searchable text format
- •Creating high-quality training data from technical manuals, textbooks, and scientific publications for domain-specific language models
- •医疗机构将MRI报告、病历等医疗文档转换为结构化数据
- •企业财务部门处理发票、合同等文档并自动移除敏感信息
- •法律机构批量数字化和分析大量合规文档或法律条文
FAQ
- Which is more popular, olmocr or text-extract-api?
- olmocr has more GitHub stars (19,687 vs 3,183).
- Which is more actively developed, olmocr or text-extract-api?
- olmocr had more commits in the last 90 days (0 vs 0).
- Should I use olmocr or text-extract-api?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.