Document Intelligence

Most valuable business data isn't in clean databases—it's trapped in PDFs, Images, Scanned Forms, and Legacy Documents. Document Intelligence is the art and science of extracting structured, actionable data from these unstructured formats.

1. The Document Intelligence Stack 🏗️

A complete Document Intelligence system has four layers:

1. Image Preprocessing: Deskewing, denoising, binarization
2. Text Extraction (OCR): Converting pixels to characters
3. Layout Analysis: Understanding document structure
4. Information Extraction: Pulling out entities, relationships, and meaning

2. OCR (Optical Character Recognition) 👁️

OCR is the foundation of document processing. It converts images of text into machine-readable text.

Modern OCR Approaches

• Traditional OCR (Tesseract):

  • Rule-based character recognition
  • Fast but struggles with handwriting, curved text, or poor quality scans
  • Best for clean, printed documents

• Deep Learning OCR (EasyOCR, PaddleOCR):

  • CNN-based character detection and recognition
  • Handles multiple languages, rotated text, and complex backgrounds
  • Better accuracy but requires more compute

• Cloud OCR APIs:

  • AWS Textract: Excellent for forms and tables
  • Google Document AI: Best for handwriting
  • Azure Form Recognizer: Specialized for invoices and receipts
  • Trade-off: Highest accuracy but costs money and requires internet

OCR Preprocessing Techniques

Before running OCR, preprocessing can dramatically improve accuracy:

3. Layout Analysis 📄

Text isn't just a stream of characters—it has visual structure that carries meaning.

Key Layout Elements

• Document Hierarchy:

  • Title, Headers (H1, H2, H3), Body text
  • Footers, Page numbers, Watermarks

• Complex Structures:

  • Tables: Rows, columns, merged cells
  • Multi-column layouts: Newspapers, scientific papers
  • Forms: Key-value pairs (Name: _____)
  • Lists: Bulleted, numbered, nested

LayoutLM Architecture

Modern layout understanding uses LayoutLM (Layout Language Model):

• Combines text, layout (bounding boxes), and visual (image) features
• Pre-trained on millions of documents
• Can classify document regions: Header, Footer, Table, Figure, etc.

4. Table Extraction (The Hard Part) 🗂️

Tables are the most valuable and most complex part of documents.

Challenges

• No clear borders: Tables without grid lines
• Merged cells: Spanning multiple rows/columns
• Nested tables: Tables within tables
• Rotated tables: Sideways orientation

Extraction Strategies

5. Text Chunking Strategies 🧩

Before feeding a 100-page PDF into an LLM or search engine, you must split it intelligently.

Strategy 1: Fixed-Size Chunking ✂️

• Method: Split every N tokens/characters
• Pros: Simple, predictable size
• Cons: Breaks sentences, loses context

Strategy 2: Recursive Chunking 🌳

• Method: Try to split by paragraphs, then sentences, then words
• Pros: Respects natural boundaries
• Cons: Still doesn't understand meaning

Strategy 3: Semantic Chunking 🧠

• Method: Embed each sentence, split when topic changes (cosine distance threshold)
• Pros: Keeps related ideas together
• Cons: More compute-intensive

6. Real-World Use Cases 🌍

Financial Services

• Extract data from invoices, receipts, bank statements
• Compliance: Process contracts for risk assessment
• OCR checks, signatures, handwritten forms

Healthcare

• Digitize patient records, prescriptions
• Extract diagnosis codes from clinical notes
• Process insurance claims

Legal

• Contract analysis (extract clauses, dates, parties)
• Discovery: Search through millions of scanned documents
• Redaction of sensitive information

Quiz

Key Takeaways

✅ OCR is just the first step—preprocessing and layout analysis are crucial.
✅ Tables are the hardest structure to extract accurately.
✅ Smart chunking with overlap preserves context for downstream tasks.
✅ Cloud APIs offer the best accuracy but cost money; open-source is improving rapidly.

What's Next?

Now that we have clean text chunks, how do we search through millions of them in milliseconds? Next Chapter: Semantic Search Systems.