# 00 - NLP for Contract Analysis: From OCR to Understanding

NLP pipeline for contract analysis: OCR, clause extraction, risk classification and automated reports.

## What you'll learn

- The pipeline is multi-stage: OCR → structuring → NER → classification → semantic analysis. Each phase has specific technologies and metrics.
- Domain-specific models (LegalBERT, CaseLaw-BERT) significantly outperform
        generic models on legal tasks. Fine-tuning on datasets like CUAD is accessible even to
        teams with limited resources.
- The hybrid rules + ML approach is most effective: rules for structured entities
        (dates, amounts, references), ML for semantics and classification.
- Human-in-the-loop is not a compromise but a necessity: AI accelerates
        legal work, it does not replace it. Human supervision is indispensable for validation
        and continuous improvement.
- Integration with CLM, DMS, and business workflows is essential to transform
        technical analysis into business value.

*This article is part of the **LegalTech** series on federicocalo.dev.*

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## Read the full article

The complete article (26 min read) with code examples, diagrams, and practical exercises is available here:

**➡️ [00 - NLP for Contract Analysis: From OCR to Understanding](https://federicocalo.dev/en/blog/nlp-contract-analysis-ocr-to-understanding)**

`https://federicocalo.dev/en/blog/nlp-contract-analysis-ocr-to-understanding`

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*By [Federico Calò](https://federicocalo.dev) — Software Developer & Technical Writer*