Fixed typo in README.md
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README.md
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@@ -26,7 +26,7 @@ It achieves the following results on the evaluation set on DIBCO metrics:
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- PSNR: 14.5040
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- DRD: 5.3749
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with PSNR the peak signal-to-noise ratio and
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For more information on the above DIBCO metrics, see the 2017 introductory [paper](https://ieeexplore.ieee.org/document/8270159).
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@@ -36,7 +36,7 @@ For more information on the above DIBCO metrics, see the 2017 introductory [pape
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This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO).
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This is in contrast to the late trend of adapting classic binarization algorithms with neural networks,
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such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or the aforementioned SauvolaNet work
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as extensions of the classical Otsu's method and Sauvola thresholding algorithm, respectively.
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## Intended uses & limitations
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- PSNR: 14.5040
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- DRD: 5.3749
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with PSNR the peak signal-to-noise ratio and DRD the distance reciprocal distortion.
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For more information on the above DIBCO metrics, see the 2017 introductory [paper](https://ieeexplore.ieee.org/document/8270159).
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This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO).
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This is in contrast to the late trend of adapting classic binarization algorithms with neural networks,
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such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or the aforementioned SauvolaNet work
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as extensions of the classical Otsu's method and Sauvola thresholding algorithm, respectively.
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## Intended uses & limitations
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