Instructions to use SBB/sbb_binarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use SBB/sbb_binarization with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("SBB/sbb_binarization") - Notebooks
- Google Colab
- Kaggle
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README.md
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# About `sbb_binarization`
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This is a model for document image binarization. It can be
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to convert all pixels in a color or grayscale document image
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only black or white pixels. The main purpose is to improve the
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contrast between foreground (text) and background (paper) for
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purposes of OCR. The model is based on a `ResNet50-Unet` model.
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# About `sbb_binarization`
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This is a CNN model for document image binarization. It can be
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used to convert all pixels in a color or grayscale document image
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to only black or white pixels. The main purpose is to improve the
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contrast between foreground (text) and background (paper) for
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purposes of OCR. The model is based on a `ResNet50-Unet` model.
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