Inquiry About OCR Model, Training Techniques, and Dataset Utilised in OpenLPR

[yihong1120]

Dear OpenLPR Team,

I hope this message finds you well. I've recently been exploring your fascinating OpenLPR project on GitHub, and I'm thoroughly impressed with the work you've put into developing this comprehensive license plate recognition system.

As I delve deeper into your project, I am particularly interested in learning more about the OCR component. Specifically, I have a few queries that I hope you could shed some light on:

1. OCR Model Details: Could you provide more information about the OCR model used in OpenLPR? I am keen to understand the architecture and the rationale behind choosing this particular model.

2. Training Methodology: What training methods were employed for the OCR model? Any insights into the training process, including the techniques and strategies used, would be invaluable.

3. Dataset Utilisation: Could you elaborate on the dataset used for training the OCR model? Information regarding the size, diversity, and source of the dataset would be greatly appreciated. Additionally, how do you ensure the dataset's robustness to various license plate formats and conditions?

Understanding these aspects would greatly aid in comprehending the overall efficiency and effectiveness of the OCR system in various scenarios.

Thank you for your time and consideration. I look forward to your response and continue to admire the work you are doing with OpenLPR.

Best regards,
yihong1120