Run a first handwritten-math-recognizer example

Start with the smallest workflow described by the README for handwritten-math-recognizer. Complete installation and configuration 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

1smart-classroom/
2├── main.py                  # Main application
3├── app.py                   # Dashboard app (train / test / camera)
4├── draw_pad.py              # Drawing pad with digit segmentation
5├── retrain_robust.py        # Trains models/expr_model.pth
6├── generate_tests.py        # Deterministic test image generator
7├── gen_demo.py              # Regenerates the README screenshots
8├── config.py                # Configuration
9├── requirements.txt         # Dependencies
10├── docs/                    # Demo screenshots
11├── utils/
12│   ├── face_detector.py     # Face detection & recognition
13│   ├── number_recognizer.py # CNN number recognition
14│   └── profile_manager.py   # Student profiles & grading
15├── models/                  # Saved ML models (gitignored)
16├── data/                    # MNIST + test data (gitignored)
17└── profiles/                # Student profile JSONs

Next task

Read the usage guide for the documented components, configuration, and source layout.

Source and help

Start with the smallest workflow described by the README for handwritten-math-recognizer. Complete installation and configuration first.

First-run instructions

Project Structure

1smart-classroom/
2├── main.py                  # Main application
3├── app.py                   # Dashboard app (train / test / camera)
4├── draw_pad.py              # Drawing pad with digit segmentation
5├── retrain_robust.py        # Trains models/expr_model.pth
6├── generate_tests.py        # Deterministic test image generator
7├── gen_demo.py              # Regenerates the README screenshots
8├── config.py                # Configuration
9├── requirements.txt         # Dependencies
10├── docs/                    # Demo screenshots
11├── utils/
12│   ├── face_detector.py     # Face detection & recognition
13│   ├── number_recognizer.py # CNN number recognition
14│   └── profile_manager.py   # Student profiles & grading
15├── models/                  # Saved ML models (gitignored)
16├── data/                    # MNIST + test data (gitignored)
17└── profiles/                # Student profile JSONs

Next task

Read the usage guide for the documented components, configuration, and source layout.