# Run a first handwritten-math-recognizer example

Start with the smallest workflow described by the README for **handwritten-math-recognizer**. Complete [installation and configuration](/docs/up-to-code-handwritten-math-recognizer/installation-guide) first.

## First-run instructions

The captured README does not include a runnable quick-start example. Use the linked source reference to identify the public entry point and its prerequisites. Do not assume the repository name is an installable package.

## Project Structure

```
smart-classroom/
├── main.py                  # Main application
├── app.py                   # Dashboard app (train / test / camera)
├── draw_pad.py              # Drawing pad with digit segmentation
├── retrain_robust.py        # Trains models/expr_model.pth
├── generate_tests.py        # Deterministic test image generator
├── gen_demo.py              # Regenerates the README screenshots
├── config.py                # Configuration
├── requirements.txt         # Dependencies
├── docs/                    # Demo screenshots
├── utils/
│   ├── face_detector.py     # Face detection & recognition
│   ├── number_recognizer.py # CNN number recognition
│   └── profile_manager.py   # Student profiles & grading
├── models/                  # Saved ML models (gitignored)
├── data/                    # MNIST + test data (gitignored)
└── profiles/                # Student profile JSONs
```

## Next task

[Read the usage guide](/docs/up-to-code-handwritten-math-recognizer/usage-guide) for the documented components, configuration, and source layout.

## Source and help

- [GitHub repository](https://github.com/Up-to-code/handwritten-math-recognizer)
- [Original README](https://github.com/Up-to-code/handwritten-math-recognizer/blob/main/README.md)
- [Issues and existing reports](https://github.com/Up-to-code/handwritten-math-recognizer/issues)