olmOCR, Marker, or PaddleOCR locally
Apache-2.0 converters like olmOCR and Marker turn messy PDFs into clean Markdown on your own hardware; Tesseract still handles plain text extraction.
Document AI
Pick OCR by Document Messiness, Privacy, and RAG Ingestion Needs.
OCR is often the first failure point in document AI. A RAG app built on bad extraction will retrieve bad chunks, cite wrong pages, and miss tables or handwriting no matter how strong the LLM is.
Last updated September 17, 2026. Test OCR on your own scans before choosing a hosted API or local engine.
Quick picks
Match OCR to document quality and privacy.
Document AI API
Hosted OCR can be better for forms, invoices, layouts, and mixed image quality, but verify cost and data terms.
Client-side OCR
Useful for small student demos where privacy and no backend setup matter more than perfect accuracy.
Preserve layout metadata
Keep page number, section, table context, and bounding clues so answers can cite the right source.
| OCR path | Best for | Strength | Tradeoff |
|---|---|---|---|
| Tesseract OCR | Local text extraction from clean scans | Open source, mature, private, no API bill | Needs preprocessing and may struggle with complex layouts |
| PaddleOCR | Multilingual OCR and stronger document extraction | Open-source toolkit with broad OCR workflows | Setup and model selection can be heavier for beginners |
| olmOCR | Bulk PDF-to-Markdown for RAG and training data | AI2 toolkit with tables, equations, handwriting, natural reading order, and header/footer removal under $200 per million pages | Needs a GPU to run the 7B vision model |
| Marker | Converting PDF, DOCX, PPTX, XLSX, and EPUB files to Markdown or JSON | Layout-aware VLM with a CPU-friendly mode, extracted images, and an optional LLM boost | Very complex nested tables and forms can still need review |
| Mistral OCR 4.1 | Hosted document AI with structured output | Paragraph-level bounding boxes, structural block labels, and block-level confidence scores via API | Premier hosted model - verify pricing and data terms |
| Browser OCR | Small demos and client-side privacy | No server key, easy to show in a portfolio | Limited performance and device-dependent speed |
| Hosted OCR API | Invoices, forms, receipts, scanned PDFs | Better layout handling and managed infrastructure | Free tier, privacy, retention, and pricing must be verified |
| Vision LLM extraction | Light layout reasoning and screenshots | Can combine OCR with reasoning over images | May be slower, less deterministic, and harder to audit |
OCR workflow
Clean extraction before you embed anything.
For RAG, OCR output becomes the source of truth. Keep a sample review step before chunking so garbage extraction does not become polished hallucination.
OCR quality checklist
- Test on real scans, not only clean sample images.
- Preprocess rotation, contrast, page splitting, and file size.
- Compare extracted text against the original page.
- Keep page number and table or heading context.
- Mark low-confidence pages for manual review.
- Only then chunk, embed, and index the text.
People also ask
OCR questions for document AI projects.
Is Tesseract enough for a student OCR project?
Yes for clean printed text and private demos. For complex forms, handwriting, tables, or low-quality scans, compare PaddleOCR or hosted document AI APIs.
Should I OCR PDFs before RAG?
Only scanned PDFs need OCR. Digital PDFs often contain extractable text, but you should still inspect parsing quality, headings, tables, and page metadata.
Can vision models replace OCR?
Modern vision LLMs like Gemini 3.8 Flash or Nano Banana 2 can extract and reason over documents directly, but classic OCR is still better when you need deterministic, auditable text extraction at scale. Tools like olmOCR and Marker use vision models exactly this way - for conversion first, reasoning later.