📚Python-Document-Preparation-and-Extraction

Scripts for reliable and scalable data preparation and extraction from unstructured documents (e.g., PDFs). Learn to build pipelines for text and table parsing, transforming complex documents into high-quality, structured datasets for product use cases.

This repository hosts a set of practical Python scripts and Jupyter notebooks focused on Document Data Engineering. The core goal is to provide reliable, scalable workflows for data preparation and extraction from unstructured document formats, primarily PDFs.

These labs are essential for software engineers and data scientists looking to feed high-quality, structured data into AI/ML models, RAG systems, or business intelligence pipelines.

🚀 Key Features

The key features of document data preparation and extraction pipeline, particularly for use in Python AI labs, are centered on converting complex, unstructured data (like PDFs) into clean, structured data suitable for machine learning models.

✨ Data preparation These features ensure the extracted data is clean, accurate, and optimized for AI tasks.

• Optical Character Recognition (OCR): The capability to convert scanned images or image-only PDFs into machine-readable text layers. This is non-negotiable for handling older or non-digital documents.

• Noise Reduction & Cleaning: Automated scripts to remove irrelevant elements like headers, footers, watermarks, page numbers, and repetitive boilerplate text, thus isolating the core content.

• Text Normalization: Standardizing whitespace, resolving character encoding issues, and correcting common OCR errors (e.g., confusing 'l' and '1') to maintain high data consistency.

• Data Validation: Implementing rules to check the extracted values (e.g., ensuring a "Date" field is in the correct format or a "Total" field is a numerical value) to guarantee accuracy.

• Chunking and Segmentation: Dividing the clean text into smaller, meaningful pieces (chunks) based on semantic boundaries (sections, paragraphs). This is crucial for RAG (Retrieval-Augmented Generation) systems and effective LLM processing.

✨ Extraction

• Robust PDF Parsing: Efficiently handles complex layouts, multi-page documents, and various encoding issues using industry-standard libraries.

• Text Extraction: Focuses on clean, ordered text extraction, minimizing garbage characters and header/footer noise.

• Tabular Data Extraction: Specialized workflows to accurately detect and extract structured tables from within documents.

• Data Preparation: Includes scripts for cleaning, chunking, and formatting extracted data for vector databases and language models.

• Tooling: Demonstrates effective use of powerful libraries like pymupdf (FitZ)/ and pdplumber.

🔬 Core Extraction Tools Used

Library	Primary Use Case
pymupdf	High-speed, robust text and image extraction from PDFs.
pdplumber	Accurate detection and extraction of tabular data from PDF files.
pydf2	General PDF manipulation (merging, splitting pages).
pandas	Data structuring, cleaning, and preparation of extracted tables.

Task (src files) Python - Colab Notebooks

• Python & Goolgle Colab

  • Review the Cleaning Mortgage Loan Data code: HERE

    • Data bank_loan.csv: HERE

  • Review the Work with JSON Data code: HERE

    • Data sample.json: HERE

  • Review the Perform text Cleaning and Standardization code: HERE

    • Data text_sample.txt: HERE

  • Review the Enhanced A Scanned Document Using Pre-processing code: HERE

    • Data noisy_image_sample.jpg: HERE

• Python Data Extraction

  • Review the Python Libraries for Data Extraction code: HERE

    • Data sample_title_report.pdf: HERE

  • Review the Resume Parser with PyMuPDF code: HERE

    • Data sample_resume.pdf: HERE

  • Review the Extract and Structure Data from Mortgage PDFs code: HERE

    • Data LenderFeesWorksheetNew: HERE

  • Review the Regular Expressions (Regex) Documentation: HERE

    • Data loan_application.txt: HERE

  • Review the Table Extraction From Sample PDF: HERE

    • Data foo.pdf: HERE

  • Review the Extract Key Fields from the Loan Worksheet: HERE

    • Data LenderFeesWorksheetNew: HERE

• Optimizing OCR

  • Review the Extracting text & Bounding Boxes from Scanned PDFs: HERE

    • Data sample_mortgage_document.pdf: HERE

  • Review the Analyze a Scanned PDF (End to End): HERE

    • Data MTG_10009588.pdf: HERE

  • Review the Layout OCR Demo: HERE

    • Data Loan Fees WorksheetNew-2: HERE

  • Review the Compare 3 OCR Engines on a Mortgage PDF: HERE

    • Data LenderFeesWorksheetNew: HERE