document-processing

Document Processing

Extract, convert, and structure content from PDFs, images, and other document formats. Handles OCR, text extraction, markdown conversion, email extraction, and structured data output.

When to Use

• Converting scanned documents to searchable text
• Extracting text from PDFs (native or scanned)
• Converting documents to markdown for further processing
• Extracting emails, addresses, or other structured data from documents
• Batch processing document collections

Processing Pipeline

Step 1: Identify Document Type

Determine the processing approach:

Input	Method	Tool
Native PDF (has text layer)	Direct extraction	pdftotext, pymupdf
Scanned PDF (images only)	OCR	Mistral OCR API, tesseract
Image files (PNG, JPG, TIFF)	OCR	Mistral OCR API, tesseract
Word documents (.docx)	Conversion	python-docx, pandoc
HTML	Conversion	pandoc, beautifulsoup4

Detection:

# Check if PDF has text content
pdftotext input.pdf - | head -20
# If output is empty or garbled, it's a scanned PDF -> use OCR

Step 2: Extract Content

Native PDF Extraction

import pymupdf

doc = pymupdf.open("input.pdf")
for page in doc:
    text = page.get_text("markdown")  # or "text", "html"
    print(text)

OCR with Mistral (for scanned documents)

Requires MISTRAL_API_KEY environment variable. Falls back to tesseract for offline processing if unavailable.

import base64
import httpx

def ocr_page(image_path: str, api_key: str) -> str:
    with open(image_path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode()

    response = httpx.post(
        "https://api.mistral.ai/v1/chat/completions",
        headers={"Authorization": f"Bearer {api_key}"},
        json={
            "model": "mistral-ocr-latest",
            "messages": [{
                "role": "user",
                "content": [
                    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_data}"}},
                    {"type": "text", "text": "Extract all text from this image. Preserve formatting, tables, and structure. Output as markdown."}
                ]
            }]
        }
    )
    return response.json()["choices"][0]["message"]["content"]

OCR with Tesseract (offline fallback)

# Single page
tesseract input.png output -l eng --oem 3 --psm 6

# PDF to text via tesseract
pdftoppm input.pdf page -png
for f in page-*.png; do tesseract "$f" "${f%.png}" -l eng; done
cat page-*.txt > output.txt

Step 3: Structure Output

Convert extracted text to structured formats:

Markdown cleanup

• Fix OCR artifacts (broken words, spurious line breaks)
• Reconstruct tables from aligned text
• Identify headers from font size/weight changes
• Preserve list formatting

Structured data extraction

# Extract emails
import re
emails = re.findall(r'[\w.+-]+@[\w-]+\.[\w.]+', text)

# Extract dates
dates = re.findall(r'\b\d{1,2}[/-]\d{1,2}[/-]\d{2,4}\b', text)

# Extract phone numbers
phones = re.findall(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b', text)

Step 4: Output

Save results in the requested format:

• Markdown (.md) - default for text content
• JSON - for structured data extraction
• Plain text (.txt) - for simple text extraction

Batch Processing

For document collections:

# Process all PDFs in a directory
for pdf in /path/to/docs/*.pdf; do
  name=$(basename "$pdf" .pdf)
  pdftotext "$pdf" "/path/to/output/${name}.txt"
done

For large collections, track progress:

1. Create a manifest of input files
2. Process each file, recording success/failure
3. Report summary (processed, failed, skipped)

Quality Checks

After extraction, verify:

• Text is readable (not garbled encoding)
• Tables preserved their structure
• No pages were skipped
• Special characters rendered correctly
• Headers and sections identified

Additional Resources

• Reference: references/ocr-configuration.md - Tesseract languages, page segmentation modes, preprocessing
• Reference: references/pdf-tools.md - Comparison of PDF extraction libraries and their tradeoffs