DiTo97 commited on
Commit
d837861
·
1 Parent(s): a4c13bc

Fixed typo in README.md

Browse files
Files changed (1) hide show
  1. README.md +2 -2
README.md CHANGED
@@ -26,7 +26,7 @@ It achieves the following results on the evaluation set on DIBCO metrics:
26
  - PSNR: 14.5040
27
  - DRD: 5.3749
28
 
29
- with PSNR the peak signal-to-noise ratio and DND the distance reciprocal distortion.
30
 
31
  For more information on the above DIBCO metrics, see the 2017 introductory [paper](https://ieeexplore.ieee.org/document/8270159).
32
 
@@ -36,7 +36,7 @@ For more information on the above DIBCO metrics, see the 2017 introductory [pape
36
 
37
  This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO).
38
  This is in contrast to the late trend of adapting classic binarization algorithms with neural networks,
39
- such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or the aforementioned SauvolaNet work,
40
  as extensions of the classical Otsu's method and Sauvola thresholding algorithm, respectively.
41
 
42
  ## Intended uses & limitations
 
26
  - PSNR: 14.5040
27
  - DRD: 5.3749
28
 
29
+ with PSNR the peak signal-to-noise ratio and DRD the distance reciprocal distortion.
30
 
31
  For more information on the above DIBCO metrics, see the 2017 introductory [paper](https://ieeexplore.ieee.org/document/8270159).
32
 
 
36
 
37
  This model is part of on-going research on pure semantic segmentation models as a formulation of document image binarization (DIBCO).
38
  This is in contrast to the late trend of adapting classic binarization algorithms with neural networks,
39
+ such as [DeepOtsu](https://arxiv.org/abs/1901.06081) or the aforementioned SauvolaNet work
40
  as extensions of the classical Otsu's method and Sauvola thresholding algorithm, respectively.
41
 
42
  ## Intended uses & limitations