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README.md
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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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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, winning 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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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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For further details, have a look at [sbb_binarization](https://github.com/qurator-spk/sbb_binarization) on GitHub.
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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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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, winning 1 task.
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# Weights
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We provide a `saved model` for Tensorflow2.
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