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PaddleOCR for configurable document recognition
Detect and recognize text and extract structure with PaddleOCR pipelines.
Key Takeaways
- The project supplies separate recognition and document-processing components as well as ready-made pipelines.
- Begin with models for the script and input type in your test set.
- Start with cropped text and one complete document page.
- An exported deployment needs compatible model artifacts and runtime dependencies.
How it works
The project supplies separate recognition and document-processing components as well as ready-made pipelines. Language, layout and deployment choices determine which models run.
What you need
Begin with models for the script and input type in your test set. Separate cropped recognition from full-page extraction so performance changes have an identifiable cause.
What to try first
Start with cropped text and one complete document page. Compare recognition output against the reference and log whether failures start in detection or transcription.
Run the same page through detection and recognition with one input variation. It is easier to diagnose the failing stage when the reference remains fixed.
Where to look for failures
An exported deployment needs compatible model artifacts and runtime dependencies. Measure accuracy on the actual scan quality and scripts you expect to receive.
Related Franklin pages
Run the same scan through each approach and inspect the failed regions. An OCR engine and a full document-conversion pipeline can differ substantially in reading order and structural output.
- Recognize printed text with a local engine and language data files. Tesseract for conventional local OCR
- Convert complex documents to structured outputs for reading or downstream processing. MinerU for extracting PDF layout and formulas
For how recognizing characters fits into the larger document processing workflow, see Franklin’s glossary. Optical character recognition
Project documentation
Try it for your workflow
Explore the project’s documentation for setup and usage, or save this page to a collection for later.
Read the project documentation ↗