mindee/doctr

Health v2 · maintenance only

Adoption not part of the score

6315 stars · 673 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

docTR is a Python OCR library that extracts text from documents and images using a two-stage deep learning approach: text detection followed by text recognition. It offers pretrained models in PyTorch and TensorFlow, layout analysis, and a simple 3-line API for end-to-end text extraction.

Use cases

• extract text from scanned documents
• run ocr on pdfs and images in python
• localize and recognize words in document images
• detect tables and layout regions in documents
• benchmark my own text recognition model against state-of-the-art ocr
• deploy an ocr api service
• convert receipts or invoices to text

When to choose

• you need high-accuracy OCR comparable to cloud services like AWS Textract but self-hosted
• you want a choice of detection and recognition architectures with pretrained weights
• you need both CPU and GPU inference with a lightweight dependency footprint
• you want layout analysis (tables, figures, headers) alongside text extraction

When to avoid

• you need handwriting transcription or full-page document understanding beyond text/layout
• you want a turnkey GUI application rather than a Python library
• you need OCR for non-Latin scripts not covered by the model zoo
• you prefer a cloud OCR API with zero model management

Facets

library · maturity active

ocr computer-vision deep-learning machine-learning image-processing computer-vision image-processing deep-learning machine-learning python cross-platform text-detection text-recognition pytorch tensorflow document-parsing layout-analysis pretrained-models natural-language-processing gpu docker

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