Update README.md

README.md

@@ -29,9 +29,9 @@ For more information on the above DIBCO metrics, see the 2017 introductory [pape
 ## Model description
 
 This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO).
-This is in contrast to the late trend of adapting classic binarization algorithms with neural networks, 
-such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or the aforementioned SauvolaNet work
-as extensions of the classical Otsu's method and Sauvola thresholding algorithm, respectively.
+This is in contrast to the late trend of adapting classical binarization algorithms with neural networks, 
+such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or [SauvolaNet](https://arxiv.org/pdf/2105.05521.pdf)
+as extensions of Otsu's method and Sauvola thresholding algorithm, respectively.
 
 ## Intended uses & limitations