https://github.com/dimiz51/ocr_license_plates

A simple OCR Deep Learning Project for license plate code detection using Tensorflow and Keras
https://github.com/dimiz51/ocr_license_plates

deeplearning ocr-recognition tensorflow

Last synced: about 2 months ago
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A simple OCR Deep Learning Project for license plate code detection using Tensorflow and Keras

• Host: GitHub
• URL: https://github.com/dimiz51/ocr_license_plates
• Owner: dimiz51
• Created: 2024-05-19T13:56:39.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-22T20:01:14.000Z (about 2 years ago)
• Last Synced: 2024-05-22T20:55:20.496Z (about 2 years ago)
• Topics: deeplearning, ocr-recognition, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 683 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Simple OCR Deep Learning example  

This repository contains a very simple example on OCR detection of license plate

codes using a Neural Network based on this example: 

- https://keras.io/examples/vision/captcha_ocr/#inference

The dataset used for this was this one:

- https://www.kaggle.com/datasets/nickyazdani/license-plate-text-recognition-dataset

## Dataset samples

![Training](readme_images/dataset.png)

## Model architecture

The base model architecture:

![Training](readme_images/architecture.png)

# Results

No image augmentations were used for the model that gave these results.

## Training metrics

![Training](readme_images/training.png)

## Predictions

Some predictions on the test set:

![Training](readme_images/predictions.png)

## Overall Performance:

- **Character Accuracy**: 0.956989247311828

- **Average Precision**: 0.965224721750613  

- **Average Recall**: 0.956989247311828  

- **Average F1**: 0.9587213522697393

## Classification Report:

| 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   |

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