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Contract & Legal Document OCR — Clause Extraction, Party Identification, Structuring

Legal-BERT + LayoutLMv3 + CUAD dataset to convert contracts into structured data

Contract Processing Pipeline

Step 1: OCR — contract image/PDF → text (use PaddleOCR, Marker, etc.)
Step 2: NLU — text → structured data (clauses, parties, deadlines, amounts)

Step 1 uses regular OCR. The key is Step 2 — understanding legal text.

Legal-BERT

BERT pre-trained on 12GB of legal text. Better at understanding legal terminology.

CUAD Dataset

500+ real contracts, 13,000 expert annotations, 41 label categories (termination, liability cap, IP ownership, etc.).

LayoutLMv3

Understands text + layout position + image together. Knows where signature blocks, date fields, clause numbers are.

Practical Pipeline

Contract PDF → Marker (text) → Legal-BERT + CUAD (clause classification) → structured JSON

Key Concepts

1

Extract contract text with OCR — general tools like Marker/PaddleOCR suffice

2

Legal language understanding with Legal-BERT — BERT pre-trained on 12GB legal text

3

Fine-tune with CUAD dataset — learn to classify 41 clause categories

4

Document structure understanding with LayoutLMv3 — process text+position+image together

Use Cases

Automated contract review — extract termination, liability, non-compete clauses Contract comparison — auto-compare clause differences between contracts Legal QA — auto-answer questions like "What are the termination conditions?"