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Tesseract for conventional local OCR
Recognize printed text with a local engine and language data files.
Key Takeaways
- Tesseract can emit plain text or outputs with positions for recognized words.
- The engine needs language data and a way to decode the incoming images.
- Run a clean scan and a skewed scan with the same expected text.
- Image resolution, rotation and language packs have a large effect on output.
How it works
Tesseract can emit plain text or outputs with positions for recognized words. Page segmentation modes change how it interprets a full page versus a single line or block.
What you need
The engine needs language data and a way to decode the incoming images. Inspect page segmentation settings when moving from isolated text regions to full pages.
What to try first
Run a clean scan and a skewed scan with the same expected text. Test segmentation settings, then compare numeric fields and reading order instead of judging the preview.
Use a short list of field identifiers and visually similar characters. A minor OCR error in a record number can be more consequential than several prose errors.
Where to look for failures
Image resolution, rotation and language packs have a large effect on output. Handle handwritten or layout-heavy documents as separate cases when planning accuracy tests.
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.
- Convert complex documents to structured outputs for reading or downstream processing. MinerU for extracting PDF layout and formulas
- Convert PDFs and supported files into Markdown, JSON or other documented formats. Marker for converting documents to structured text
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 ↗