# TESTING

## Test Suites & Scripts
The application uses diagnostic and simulation scripts rather than traditional `unittest` or `pytest` suites due to the heavy reliance on Computer Vision and large video downloads.

- `test_pipeline.py`: Acts as the primary integration test, running the e2e extraction over known sample URLs to verify no missing sections or regressions occur.
- `scripts/debug/rigorous_validator.py`: A rigid assertion script used locally to guarantee extracted sequences don't fail OCR checks and maintain strict monotonicity.
- `scripts/debug/test_full_ocr.py`: Isolated test bench for verifying EasyOCR accuracy and tuning bounding box coordinates before baking them into the main pipeline.

## Validation Methodologies
Because validating computer vision outputs is visually subjective, 'tests' in this repository focus heavily on output metrics:
- Number of discrete pages extracted vs expected.
- Strict ascending sequence of OCR read measure numbers.
- Absence of specific moving artifacts (e.g., the red/blue 'Playhead cursor').
