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File IJDAR/prediction.tex

 \end{figure}
 
 
-Table \ref{selectionRes} presents some f-score statistics obtained from binarizing the DIBCO dataset. The first line corresponds to the best theoretical f-scores (having the ground truth, we know for each image the binarization method that will provide the best f-score). The second line corresponds to the f-scores obtained using only Shijian's method. The last line corresponds to the f-scores obtained using our automatic binarization selection.
+More generally, table \ref{selectionRes} presents some f-score statistics obtained from binarizing the DIBCO dataset. The first line corresponds to the best theoretical f-scores (having the ground truth, we know for each image the binarization method that will provide the best f-score). The second line corresponds to the f-scores obtained using only Shijian's method. The last line corresponds to the f-scores obtained using our automatic binarization selection.
 
-We analyse the accuracy of our binarization method selection algorithms in several ways. First, the method has a slightly better (2\%) mean accuracy than using only Shijian's method. Importantly, note that our algorithm has a higher global accuracy (the standard deviation equals $0.04$). Last, the worst binarization result of our method is much higher than Shijian's (56\%).
-Second, we compared our method with the optimal selection that we can compute from the ground truth. The results are very similar, indicating that the prediction models are accurate enough to select the best binarization method for each image (70\% perfect match). The mean error of our method is $0.009$ (standard deviation equals $0.02$), and, the worst error equals $0.06$.
+We analyse the accuracy of our binarization method selection algorithms in several ways. As expected, the method has only a slightly better (2\%) mean accuracy than using only Shijian's method. What is significant is that the standard deviation lowers from $0.12$ to $0.04$. It means that the worst binarization result of our method is much higher than Shijian's (56\%).
+We also compared our method with the optimal selection that we can compute from the ground truth. The results are very similar, indicating that the prediction models are accurate enough to select the best binarization method for each image (70\% perfect match). The mean error of our method is $0.009$ (standard deviation equals $0.02$), and, the worst error equals $0.06$.