This repository contains a very simple example on OCR detection of license plate codes using a Neural Network based on this example:
The dataset used for this was this one:
The base model architecture:
No image augmentations were used for the model that gave these results.
Some predictions on the test set:
- Character Accuracy: 0.956989247311828
- Average Precision: 0.965224721750613
- Average Recall: 0.956989247311828
- Average F1: 0.9587213522697393
| Character | Precision | Recall | F1-Score |
|---|---|---|---|
| 0 | 1.00 | 0.94 | 0.97 |
| 1 | 0.95 | 1.00 | 0.97 |
| 2 | 0.93 | 0.93 | 0.93 |
| 3 | 1.00 | 1.00 | 1.00 |
| 4 | 1.00 | 1.00 | 1.00 |
| 5 | 1.00 | 1.00 | 1.00 |
| 6 | 0.94 | 1.00 | 0.97 |
| 7 | 1.00 | 0.94 | 0.97 |
| 8 | 0.83 | 0.83 | 0.83 |
| 9 | 1.00 | 1.00 | 1.00 |
| B | 1.00 | 1.00 | 1.00 |
| C | 1.00 | 0.67 | 0.80 |
| D | 0.50 | 1.00 | 0.67 |
| E | 1.00 | 1.00 | 1.00 |
| F | 1.00 | 1.00 | 1.00 |
| H | 1.00 | 1.00 | 1.00 |
| J | 1.00 | 1.00 | 1.00 |
| K | 1.00 | 1.00 | 1.00 |
| L | 1.00 | 1.00 | 1.00 |
| M | 1.00 | 1.00 | 1.00 |
| N | 0.75 | 1.00 | 0.86 |
| P | 1.00 | 1.00 | 1.00 |
| Q | 1.00 | 1.00 | 1.00 |
| R | 1.00 | 1.00 | 1.00 |
| S | 1.00 | 1.00 | 1.00 |
| T | 1.00 | 1.00 | 1.00 |
| U | 0.67 | 0.50 | 0.57 |
| V | 1.00 | 1.00 | 1.00 |
| W | 1.00 | 1.00 | 1.00 |
| X | 1.00 | 1.00 | 1.00 |
| Y | 1.00 | 1.00 | 1.00 |
| Z | 1.00 | 0.67 | 0.80 |