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--- |
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language: |
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- zgh |
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- ber |
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tags: |
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- OCR |
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pipeline_tag: image-to-text |
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--- |
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<p align="center"> |
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<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> |
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</p> |
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**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch** |
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## Task: recognition |
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https://github.com/mindee/doctr |
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### Example usage: |
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```python |
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>>> from doctr.io import DocumentFile |
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>>> from doctr.models import ocr_predictor, from_hub |
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>>> img = DocumentFile.from_images(['<image_path>']) |
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>>> # Load your model from the hub |
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>>> model = from_hub('mindee/my-model') |
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>>> # Pass it to the predictor |
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>>> # If your model is a recognition model: |
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>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', |
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>>> reco_arch=model, |
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>>> pretrained=True) |
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>>> # If your model is a detection model: |
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>>> predictor = ocr_predictor(det_arch=model, |
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>>> reco_arch='crnn_mobilenet_v3_small', |
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>>> pretrained=True) |
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>>> # Get your predictions |
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>>> res = predictor(img) |
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``` |
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### Run Configuration |
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{ |
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"arch": "crnn_mobilenet_v3_large", |
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"train_path": "train", |
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"val_path": "val", |
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"train_samples": 1000, |
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"val_samples": 20, |
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"font": "FreeMono.ttf,FreeSans.ttf,FreeSerif.ttf", |
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"min_chars": 1, |
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"max_chars": 12, |
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"name": "crnn_mobilenet_v3_large_gen_hw", |
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"epochs": 3, |
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"batch_size": 64, |
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"device": null, |
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"input_size": 32, |
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"lr": 0.001, |
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"weight_decay": 0, |
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"workers": 2, |
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"resume": "crnn_mobilenet_v3_large_printed.pt", |
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"vocab": "tamazight", |
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"test_only": false, |
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"show_samples": false, |
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"wb": true, |
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"push_to_hub": true, |
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"pretrained": false, |
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"sched": "cosine", |
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"amp": false, |
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"find_lr": false |
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} |
