# LP Recognizer

## What is LicensePlateRecognizer?

`LicensePlateRecognizer` processes images and video frames independently without temporal tracking. It detects license plates and recognizes text in single images, image batches, or video streams (frame-by-frame via `predict_batch()`).

## When to Use LicensePlateRecognizer

Choose `LicensePlateRecognizer` when you need to:

* Process collections of images or photos
* Analyze video files frame-by-frame without temporal tracking
* Run batch recognition on multiple images or video streams
* Work without real-time tracking requirements

**For video with persistent vehicle tracking and Bayesian multi-frame text fusion**, use [LicensePlateTracker](https://docs.degirum.com/vehicle-analytics/guides/overview-1) instead - it maintains vehicle identities across frames and supports real-time alerts.

## Core Concepts

### Configuration

All settings are controlled through `LicensePlateRecognizerConfig`:

```python
import degirum_vehicle

config = degirum_vehicle.LicensePlateRecognizerConfig(
    license_plate_detection_model_spec=detection_spec,  # Detection model
    license_plate_ocr_model_spec=ocr_spec,             # OCR model
)

lpr = degirum_vehicle.LicensePlateRecognizer(config)
```

See [Configuration Guide](https://docs.degirum.com/vehicle-analytics/guides/overview/configuration) for all options.

## Methods

`LicensePlateRecognizer` provides methods for single-frame and batch processing:

* [**predict()**](https://docs.degirum.com/vehicle-analytics/guides/methods#predict) - Recognize license plates in a single image
* [**predict\_batch()**](https://docs.degirum.com/vehicle-analytics/guides/methods#predict_batch) - Process multiple images or video frames

See [Methods Reference](https://docs.degirum.com/vehicle-analytics/guides/overview/methods) for complete API documentation with examples.