Document AI and OCR

Document Processing Pipeline

[Document/Image]
     |
     v
[OCR: Image to Text]
     |
     v
[Layout Analysis]
     |
     v
[Structure Extraction]
     |
     v
[LLM Understanding]

OCR Options

PDF Processing

# Extract text from PDF
from pypdf import PdfReader

def extract_pdf_text(path: str) -> str:
    reader = PdfReader(path)
    text = ""
    for page in reader.pages:
        text += page.extract_text()
    return text

Vision LLM for Documents Use multimodal LLMs to understand document images:

def analyze_document_image(image_path: str, question: str) -> str:
    return llm.generate_with_image(
        image=image_path,
        prompt=f"Analyze this document and answer: {question}"
    )

Table Extraction

def extract_tables(document: str) -> list:
    return llm.generate(f"""
Extract all tables from this document as JSON arrays.
Each table should have headers and rows.

Document:
{document}

Tables (JSON):
    """)

Document Understanding Tasks

Chunking Strategies for PDFs

def chunk_pdf(pdf_path: str) -> list:
    chunks = []

    # By page
    for page in extract_pages(pdf_path):
        chunks.append({"type": "page", "content": page})

    # By section (using headers)
    sections = detect_sections(pdf_text)
    for section in sections:
        chunks.append({"type": "section", "title": section.title, "content": section.text})

    return chunks

Best Practices

• Preprocess images (deskew, denoise) before OCR
• Combine OCR with layout analysis for tables
• Use multimodal LLMs for complex documents
• Validate extracted data against expected formats
• Handle multi-page documents appropriately