Document AI – Structured Data Extraction from Documents

Automated extraction of structured information from semi-structured documents using OCR, layout detection, and rule-based post-processing.

This project converts scanned documents into machine-readable structured data, enabling automation of document processing workflows.

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Project Overview

Many business processes rely on documents such as:

• vehicle registration documents
• invoices
• insurance forms
• certificates
• identification records

These documents contain valuable information but exist only as images or PDFs.

This project builds a Document AI pipeline that extracts key fields from such documents and converts them into structured output.

Example extracted fields include:

• License plate number
• Owner name
• Address
• Vehicle type
• Vehicle model
• VIN number
• Birth date
• Insurance company
• Color
• Registration number

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Example Result

Below is an example of the system extracting structured fields from a vehicle registration document.

The pipeline identifies relevant regions, extracts text, and maps the information into structured fields.

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Pipeline Architecture

The system follows a multi-stage document intelligence workflow:

1️⃣ Input Document

• Scanned document or image

2️⃣ Preprocessing

• Image cleanup
• Noise reduction
• resizing / normalization
• preparing document for OCR

3️⃣ OCR Extraction

• text detection
• text recognition

4️⃣ Field Detection

• identifying key-value regions
• mapping extracted text to structured fields

5️⃣ Post Processing

• text normalization
• rule-based validation
• formatting corrections

6️⃣ Structured Output

Example output:

{
  "license_plate": "XX 000 000",
  "owner_name": "Max Mustermann",
  "address": "Musterstrasse 123, 9999 Beispielstadt",
  "vehicle_type": "Musterfahrzeug",
  "vehicle_model": "Sample Car 1.0",
  "vin": "VIN-DEMO-PLACEHOLDER",
  "birth_date": "1990-01-01",
  "insurance": "Demo Versicherung Muster AG",
  "vehicle_color": "Musterblau"
}

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Tech Stack

Python OpenCV – image preprocessing OCR – text recognition (Tesseract / EasyOCR / PaddleOCR) NumPy / Pandas – data processing Custom extraction logic – field detection & validation

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Project Structure

document-ai-extraction/
│
├── data/
│   ├── sample_documents
│
├── notebooks/
│   └── docAI.ipynb
│
├── src/
│   ├── preprocessing.py
│   ├── ocr_pipeline.py
│   ├── extraction_logic.py
│
├── assets/
│   └── doc_ai_example.png
│
├── requirements.txt
└── README.md

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Installation

Clone repository

git clone https://github.com/MuntahaShams/document-ai-extraction.git
cd document-ai-extraction

Install dependencies

pip install -r requirements.txt

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Running the Project

Run the notebook:

docAI.ipynb

Or run extraction on a document:

python extract.py --input sample_document.png

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Challenges in Document AI

Some challenges addressed in this project:

• noisy scans
• inconsistent document layouts
• OCR recognition errors
• multilingual fields
• varying formats across documents

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Possible Improvements

Future enhancements include:

• layout-aware models (LayoutLM / Donut)
• key-value detection models
• table extraction
• confidence scoring
• human-in-the-loop validation UI
• API deployment

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Author

Muntaha Shams AI Engineer – LLMs | NLP | Computer Vision | Document AI

GitHub https://github.com/MuntahaShams

Portfolio https://muntahashams.github.io/portfolio/projects

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