Pack OCR et Analyse de Documents

What's in this pack

This is the pipeline a working engineer would assemble in one afternoon to convert messy documents — scanned invoices, academic PDFs, screenshots, mixed-language contracts — into clean structured data. The order matters: every stage's output is the next stage's input, and skipping layout detection is the single most common reason a doc-AI pipeline produces garbage.

All ten picks are open-source and actively maintained as of 2026. The combined install is large (model weights run a few GB), but you can usually pick one tool per stage and skip the rest. Treat this pack as a menu, not a checklist.

Install in this order

1. Marker — convert PDF to Markdown end-to-end. Start here. Marker handles layout + OCR + tables + math in one shot and is the right default for most academic, technical, and structured PDFs. If Marker's output is good enough, you can stop reading.
2. Surya — document OCR for 90+ languages with layout analysis, table detection, reading-order, and LaTeX OCR. Powers Marker internally; use it standalone when you need the OCR layer without the full Markdown pipeline.
3. MinerU — extract LLM-ready data from any document. Stronger than Marker on complex layouts (multi-column papers, magazines, government forms). 57K+ GitHub stars. Picks up where Marker gives up.
4. Zerox — zero-shot PDF OCR for AI pipelines. Sends page images to a vision LLM (GPT-4o, Claude, Gemini) and gets Markdown back. Pay-per-call instead of GPU-heavy local inference. Fastest path to working when you don't want to host a model.
5. Nougat — neural optical understanding for academic documents. Meta's transformer model trained on arXiv. Best-in-class for math-heavy PDFs (equations come back as LaTeX, not garbled glyphs). Slower than Marker but more accurate on STEM papers.
6. Docling — IBM's document parsing library. Converts PDFs, DOCX, PPTX, images, and HTML into structured Markdown or JSON. The most general-purpose parser in the pack — use when your input format isn't always PDF.
7. Unstructured — document ETL for LLM pipelines. Handles 25+ file types with a unified API, partitioning text into typed elements (Title, NarrativeText, Table, ListItem). The boring industrial-strength backbone for RAG ingestion at scale.
8. OpenDataLoader PDF — AI-ready document parser focused on producing clean structured output for downstream agents. Lighter footprint than Marker/MinerU, useful when latency matters more than peak accuracy.
9. PaddleOCR — production-ready OCR for 100+ languages, including the best open-source Chinese OCR available. Use as the OCR layer when Marker/Surya struggle with non-Latin scripts or extreme noise.
10. Tesseract OCR — the 40-year-old workhorse. Slow, sometimes inaccurate on modern fonts, but predictable, scriptable, and runs on a Raspberry Pi. Keep it as your fallback when GPU isn't available and accuracy expectations are modest.

How they fit together

Document in (PDF / scan / image / DOCX)
   │
   ├─ Marker  ─────────────────► clean Markdown (try this first)
   │
   │  if Marker output is bad:
   │
   ├─ MinerU  ─────────────────► Markdown / JSON (complex layouts)
   │
   │  if input is multi-format (DOCX, PPTX, HTML):
   │
   ├─ Docling  ────────────────► structured Markdown
   ├─ Unstructured  ───────────► typed elements (Title, Table, NarrativeText)
   │
   │  if input is math-heavy academic PDF:
   │
   ├─ Nougat  ─────────────────► LaTeX + Markdown
   │
   │  if cloud LLM is cheaper than GPU:
   │
   ├─ Zerox  ──────────────────► Markdown via vision LLM
   │
   │  low-level OCR layer (called by others):
   │
   ├─ Surya / PaddleOCR / Tesseract  ──► raw text + bounding boxes
   │
   └─ OpenDataLoader PDF  ─────► lightweight structured JSON

The install pattern is: Marker first as the default, MinerU as the escalation for layouts Marker struggles with, Nougat for math, and Zerox when you want to skip GPU hosting entirely. The OCR-only tools (Surya, PaddleOCR, Tesseract) are the building blocks underneath — you call them directly when the higher-level parsers fall short on your specific document class.

Tradeoffs you'll hit

• Marker vs MinerU — Marker is faster and produces cleaner Markdown on well-behaved PDFs. MinerU handles weirder layouts (Chinese newspapers, government forms, scanned books) but takes longer and outputs noisier Markdown. Benchmark both on 10 real documents from your domain before committing.
• Local model vs vision LLM (Zerox) — A 4090 running Marker costs more upfront but is roughly an order of magnitude cheaper per page once you exceed a few thousand pages/month. Below that volume, Zerox via GPT-4o or Claude is usually the right call.
• Surya vs PaddleOCR vs Tesseract — Surya is the modern default. PaddleOCR wins on Chinese, Japanese, Korean, and Arabic. Tesseract wins on "runs anywhere with no GPU" — keep it in your pipeline as the last-resort fallback.
• Docling vs Unstructured — Docling produces cleaner Markdown; Unstructured produces typed elements better suited to RAG chunking. Use Docling when a human will read the output. Use Unstructured when only a retriever will.

Common pitfalls

• Skipping layout detection — Running raw Tesseract on a two-column academic PDF interleaves text from both columns. Always run a layout-aware tool first (Marker, Surya, MinerU) — never feed full pages to OCR blindly.
• Trusting the table output without verification — Every tool in this pack still loses cells on borderless tables, merged headers, or rotated text. Pipe table output through a quick sanity check (row count, column count, numeric column dtype) before downstream use.
• GPU memory exhaustion — Marker, MinerU, and Nougat all want 8-12 GB VRAM at full quality. On a 16 GB card, run them sequentially, not in parallel.
• Mixed-language documents — Most tools auto-detect language per page, not per region. A bilingual contract with English on the left and Chinese on the right often gets one language identified and the other mangled. PaddleOCR handles this best; for everything else, pre-segment by region.
• Forgetting to dedupe headers/footers — Marker and friends extract page numbers, running headers, and footnotes as body text. Strip them with a post-processing pass keyed on repeating substrings across pages.