CRNN Persian License Plate Recognition Model V2
| Property | Value |
|---|---|
| Language | Persian (Farsi) |
| Framework | Hezar |
| Downloads | 20,647 |
| Task | Image-to-Text (OCR) |
What is crnn-fa-license-plate-recognition-v2?
This is a specialized OCR (Optical Character Recognition) model designed specifically for Persian license plate recognition. It's built on CRNN (Convolutional Recurrent Neural Network) architecture and has been fine-tuned on a dedicated Persian license plate dataset. The model represents an advanced solution for automated license plate reading systems in Persian-speaking regions.
Implementation Details
The model is implemented using the Hezar framework and is fine-tuned from the base model 'crnn-fa-printed-96-long'. It specifically handles cropped license plate images, requiring a separate detection step before OCR processing. The implementation focuses on processing Persian characters and numbers in the standard license plate format.
- Built on CRNN architecture for optimal text recognition
- Fine-tuned on specialized Persian license plate dataset
- Requires pre-cropped license plate images
- Seamless integration with Hezar framework
Core Capabilities
- Accurate recognition of Persian license plate characters
- Handles mixed numerical and Persian character formats
- Optimized for standard Persian license plate layouts
- Easy deployment through Hezar framework
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Persian license plate recognition, offering high accuracy for this specialized use case. Its CRNN architecture and focused training make it particularly effective for processing Persian characters and numbers in license plate formats.
Q: What are the recommended use cases?
The model is ideal for automated traffic management systems, parking solutions, and security applications in Persian-speaking regions. It should be used as part of a pipeline where license plates are first detected and cropped before being processed by this OCR model.
