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 |