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--- |
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license: apache-2.0 |
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language: |
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- fa |
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library_name: hezar |
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tags: |
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- hezar |
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- image-to-text |
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pipeline_tag: image-to-text |
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--- |
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A CRNN model for Persian OCR. This model is based on a simple CNN + LSTM architecture inspired by [this paper](https://arxiv.org/abs/1507.05717). |
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This is a successor model to our previous model [hezarai/crnn-base-fa-64x256](https://huggingface.co/hezarai/crnn-base-fa-64x256). |
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The improvements include: |
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- 5X larger dataset |
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- Change input image size from 64x256 to 32x384 |
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- Increase max output length from 64 to 96 (Max length of the samples in the dataset was 48 to handle CTC loss issues) |
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- Support numbers and special characters (see id2label in `model_config.yaml`) |
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- Auto-handling of LTR characters like digits in between the text |
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Note that this model is only optimized for printed/scanned documents and works best on texts with a length of up to 50-ish characters. (For an end-to-end OCR pipeline, use a text detector model first to |
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extract text boxes preferrably in word-level and then use this model), but it can be used to be fine-tuned on other domains like license plate or handwritten texts. |
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#### Usage |
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``` |
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pip install hezar |
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``` |
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```python |
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from hezar import Model |
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crnn = Model.load("hezarai/crnn-fa-printed-96-long") |
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texts = crnn.predict(["sample_image.jpg"]) |
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print(texts) |
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``` |
