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README.md
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license: apache-2.0
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license: apache-2.0
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# About `sbb_binarization`
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This is a model for document image binarization. It can be used
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to convert all pixels in a color or grayscale document image to
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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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# Results
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In the *DocEng’2021 Time-quality binarization competition*
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([paper](https://dib.cin.ufpe.br/docs/DocEng21_bin_competition_report.pdf)),
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the model ranked 12 times under the top 8 of 63 methods in the
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OCR-related quality scores, and won 2 tasks.
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In the *ICDAR 2021 Competition on Time-Quality Document Image
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Binarization* ([paper](https://dib.cin.ufpe.br/docs/papers/ICDAR2021-TQDIB_final_published.pdf)),
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the model ranked 2 times under the top 20 of 61 methods,
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and won 1 task.
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For details, see [sbb_binarization](https://github.com/qurator-spk/sbb_binarization) on GitHub.
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# Weights
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We provide a `saved model` for Tensorflow2.
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| Model | Downloads
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| -------------| ------------------------
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| `2021_03_09` | [`saved_model`](https://huggingface.co/SBB/sbb_binarization/tree/main/saved_model)
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