| name | PDF Processing Pro |
| description | Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environments, processing large volumes of PDFs, or requiring robust error handling and validation. |
PDF Processing Pro
Production-ready PDF processing toolkit with pre-built scripts, comprehensive error handling, and support for complex workflows.
Quick start
Extract text from PDF
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
text = pdf.pages[0].extract_text()
print(text)
Analyze PDF form (using included script)
python scripts/analyze_form.py input.pdf --output fields.json
Fill PDF form with validation
python scripts/fill_form.py input.pdf data.json output.pdf
Extract tables from PDF
python scripts/extract_tables.py report.pdf --output tables.csv
Features
✅ Production-ready scripts
All scripts include:
- Error handling: Graceful failures with detailed error messages
- Validation: Input validation and type checking
- Logging: Configurable logging with timestamps
- Type hints: Full type annotations for IDE support
- CLI interface:
--help flag for all scripts
- Exit codes: Proper exit codes for automation
✅ Comprehensive workflows
- PDF Forms: Complete form processing pipeline
- Table Extraction: Advanced table detection and extraction
- OCR Processing: Scanned PDF text extraction
- Batch Operations: Process multiple PDFs efficiently
- Validation: Pre and post-processing validation
Advanced topics
PDF Form Processing
For complete form workflows including:
- Field analysis and detection
- Dynamic form filling
- Validation rules
- Multi-page forms
- Checkbox and radio button handling
See FORMS.md
Table Extraction
For complex table extraction:
- Multi-page tables
- Merged cells
- Nested tables
- Custom table detection
- Export to CSV/Excel
See TABLES.md
OCR Processing
For scanned PDFs and image-based documents:
- Tesseract integration
- Language support
- Image preprocessing
- Confidence scoring
- Batch OCR
See OCR.md
Included scripts
Form processing
analyze_form.py - Extract form field information
python scripts/analyze_form.py input.pdf [--output fields.json] [--verbose]
fill_form.py - Fill PDF forms with data
python scripts/fill_form.py input.pdf data.json output.pdf [--validate]
validate_form.py - Validate form data before filling
python scripts/validate_form.py data.json schema.json
Table extraction
extract_tables.py - Extract tables to CSV/Excel
python scripts/extract_tables.py input.pdf [--output tables.csv] [--format csv|excel]
Text extraction
extract_text.py - Extract text with formatting preservation
python scripts/extract_text.py input.pdf [--output text.txt] [--preserve-formatting]
Utilities
merge_pdfs.py - Merge multiple PDFs
python scripts/merge_pdfs.py file1.pdf file2.pdf file3.pdf --output merged.pdf
split_pdf.py - Split PDF into individual pages
python scripts/split_pdf.py input.pdf --output-dir pages/
validate_pdf.py - Validate PDF integrity
python scripts/validate_pdf.py input.pdf
Common workflows
Workflow 1: Process form submissions
python scripts/analyze_form.py template.pdf --output schema.json
python scripts/validate_form.py submission.json schema.json
python scripts/fill_form.py template.pdf submission.json completed.pdf
python scripts/validate_pdf.py completed.pdf
Workflow 2: Extract data from reports
python scripts/extract_tables.py monthly_report.pdf --output data.csv
python scripts/extract_text.py monthly_report.pdf --output report.txt
Workflow 3: Batch processing
import glob
from pathlib import Path
import subprocess
for pdf_file in glob.glob("invoices/*.pdf"):
output_file = Path("processed") / Path(pdf_file).name
result = subprocess.run([
"python", "scripts/extract_text.py",
pdf_file,
"--output", str(output_file)
], capture_output=True)
if result.returncode == 0:
print(f"✓ Processed: {pdf_file}")
else:
print(f"✗ Failed: {pdf_file} - {result.stderr}")
Error handling
All scripts follow consistent error patterns:
result = subprocess.run(["python", "scripts/fill_form.py", ...])
if result.returncode == 0:
print("Success")
elif result.returncode == 4:
print("Validation failed - check input data")
else:
print(f"Error occurred: {result.returncode}")
Dependencies
All scripts require:
pip install pdfplumber pypdf pillow pytesseract pandas
Optional for OCR:
Performance tips
- Use batch processing for multiple PDFs
- Enable multiprocessing with
--parallel flag (where supported)
- Cache extracted data to avoid re-processing
- Validate inputs early to fail fast
- Use streaming for large PDFs (>50MB)
Best practices
- Always validate inputs before processing
- Use try-except in custom scripts
- Log all operations for debugging
- Test with sample PDFs before production
- Set timeouts for long-running operations
- Check exit codes in automation
- Backup originals before modification
Troubleshooting
Common issues
"Module not found" errors:
pip install -r requirements.txt
Tesseract not found:
Memory errors with large PDFs:
with pdfplumber.open("large.pdf") as pdf:
for page in pdf.pages:
text = page.extract_text()
Permission errors:
chmod +x scripts/*.py
Getting help
All scripts support --help:
python scripts/analyze_form.py --help
python scripts/extract_tables.py --help
For detailed documentation on specific topics, see: