{"id":25833835,"url":"https://github.com/dimiz51/ocr_license_plates","last_synced_at":"2026-05-13T15:38:54.324Z","repository":{"id":240529185,"uuid":"802872279","full_name":"dimiz51/OCR_License_plates","owner":"dimiz51","description":"A simple OCR Deep Learning Project for license plate code detection using Tensorflow and Keras","archived":false,"fork":false,"pushed_at":"2024-05-22T20:01:14.000Z","size":699,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-05-22T20:55:20.496Z","etag":null,"topics":["deeplearning","ocr-recognition","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Simple OCR Deep Learning example  \nThis repository contains a very simple example on OCR detection of license plate\ncodes using a Neural Network based on this example: \n- https://keras.io/examples/vision/captcha_ocr/#inference\n\nThe dataset used for this was this one:\n- https://www.kaggle.com/datasets/nickyazdani/license-plate-text-recognition-dataset\n\n## Dataset samples\n![Training](readme_images/dataset.png)\n\n## Model architecture\nThe base model architecture:\n\n![Training](readme_images/architecture.png)\n\n# Results\nNo image augmentations were used for the model that gave these results.\n\n## Training metrics\n![Training](readme_images/training.png)\n\n\n## Predictions\nSome predictions on the test set:\n\n![Training](readme_images/predictions.png)\n\n\n## Overall Performance:\n\n- **Character Accuracy**: 0.956989247311828\n- **Average Precision**: 0.965224721750613  \n- **Average Recall**: 0.956989247311828  \n- **Average F1**: 0.9587213522697393\n\n## Classification Report:\n\n| Character | Precision | Recall | F1-Score |\n|-------|-----------|--------|----------|\n|   0   |   1.00    |  0.94  |   0.97   |\n|   1   |   0.95    |  1.00  |   0.97   |\n|   2   |   0.93    |  0.93  |   0.93   |\n|   3   |   1.00    |  1.00  |   1.00   |\n|   4   |   1.00    |  1.00  |   1.00   |\n|   5   |   1.00    |  1.00  |   1.00   |\n|   6   |   0.94    |  1.00  |   0.97   |\n|   7   |   1.00    |  0.94  |   0.97   |\n|   8   |   0.83    |  0.83  |   0.83   |\n|   9   |   1.00    |  1.00  |   1.00   |\n|   B   |   1.00    |  1.00  |   1.00   |\n|   C   |   1.00    |  0.67  |   0.80   |\n|   D   |   0.50    |  1.00  |   0.67   |\n|   E   |   1.00    |  1.00  |   1.00   |\n|   F   |   1.00    |  1.00  |   1.00   |\n|   H   |   1.00    |  1.00  |   1.00   |\n|   J   |   1.00    |  1.00  |   1.00   |\n|   K   |   1.00    |  1.00  |   1.00   |\n|   L   |   1.00    |  1.00  |   1.00   |\n|   M   |   1.00    |  1.00  |   1.00   |\n|   N   |   0.75    |  1.00  |   0.86   |\n|   P   |   1.00    |  1.00  |   1.00   |\n|   Q   |   1.00    |  1.00  |   1.00   |\n|   R   |   1.00    |  1.00  |   1.00   |\n|   S   |   1.00    |  1.00  |   1.00   |\n|   T   |   1.00    |  1.00  |   1.00   |\n|   U   |   0.67    |  0.50  |   0.57   |\n|   V   |   1.00    |  1.00  |   1.00   |\n|   W   |   1.00    |  1.00  |   1.00   |\n|   X   |   1.00    |  1.00  |   1.00   |\n|   Y   |   1.00    |  1.00  |   1.00   |\n|   Z   |   1.00    |  0.67  |   0.80   |\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdimiz51%2Focr_license_plates","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdimiz51%2Focr_license_plates","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdimiz51%2Focr_license_plates/lists"}