from omniparser import OmniParser
parser = OmniParser(language="en")
result = parser.parse("./samples/invoice.pdf")
print(result.to_json(indent=2))

result.save_json("./outputs/invoice.json")

parser = OmniParser(language="en", ocr_model="tesseract", detect_rotation=True)

result = parser.parse("./samples/invoice.pdf", extract_tables=True)
for table in result.tables:
df = table.to_dataframe
print(df.head)

header_map = {
"item": , you can:
- Chat over code snippets and PDFs you’re testing
- Generate quick adapters (e.g., header normalizers, regex templates)
- Summarize parsing results and spot anomalies before you build dashboards
It’s not a replacement for OmniParser—but it’s a powerful companion while you prototype, debug, and document your pipeline.
---
## Action Plan: Turn Tutorials into Production Wins
- Pick 3 tutorials aligned with your highest-impact documents.
- Create a small validation suite (10–20 docs) and run it after each change.
- Add a review queue for low-confidence fields; measure resolution time.
- Log normalization rules and edge cases; convert them into templates.
- Schedule a monthly benchmark to catch drift and regressions.
---
## Key Takeaways
- The best OmniParser tutorials combine code, heuristics, and production concerns.
- Start small (Quickstart), then go deep (Tables, Layout, Validation).
- Preprocessing and bounding boxes dramatically improve accuracy on messy scans.
- Productionizing means caching, batching, retries, and measurable quality.
- A lightweight AI assistant like [Sider.AI](https://sider.ai) can accelerate experimentation and documentation.
---
## Appendix: Starter Repo Structure (Optional)
```text
omniparser-starter/
├─ app/
│ ├─ api.py
<a37>│ ├─ workers.py</a36>
│ └─ validators.py
├─ notebooks/
│ ├─ 01_quickstart.ipynb
│ ├─ 02_tables.ipynb
│ └─ 03_preprocessing.ipynb
├─ samples/
│ ├─ invoice.pdf
│ ├─ receipt.jpg
│ └─ statement.pdf
├─ outputs/
└─ .cache/