# FADIT: Fast Document Image Thresholding
## 5 Conclusions  
Segmentation of text from degraded document images is a very challenging task. Experimentally, FADIT is compared against [PERSON]'s and [PERSON] et al.'s thresholding techniques. Although image thresholding has been popular in image segmentation for over 50 years, the classic [PERSON]'s and [PERSON] et al.'s methods still produce relatively better results than others [8]. FADIT obtains a better segment results than two classic methods and has a lower computational complexity. FADIT is a nonparametric technique to find the optimal threshold through the optimization of a new criterion function, and is a simple and robust algorithm for document image thresholding. Experimental results of FADIT can properly segment out text from background and they are quite close to the ground-truth images.  
Since it does well in binary segmentation, FADIT can be applied to some specific applications, e.g., saliency detection. Most saliency detection tasks use a very clear object in an image and the image is captured by focusing the object in attention. FADIT can be used to produce a reference results for training a neural network in unsupervised way. Furthermore, FADIT can be used to generate pseudo labels for weakly labeled segmentation tasks. Since FADIT is good at segmenting binary targets and many medical image segmentation tasks only need to segment a specific organ, and medical images can be segmented by FADIT, at least it can obtain pseudo labels for training a neural networks.  
[PERSON] did the experimental results and wrote the manuscript. [PERSON] discussed with [PERSON] and gave many useful suggestions. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 13 th Five-year Informatization Plan of the Chinese Academy of Sciences, Grant Nos. XXH13506 and XXH13505-220, aAnd Data sharing fundamental program for Construction of the National Science and Technology Infrastructure Platform (Y719H71006). Technical support was provided by the National Cryosphere and Desert Scientific Data Center of China.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{5}{c}{**Peak Signal-To-Noise Ratio (dB)**} \\ \hline  & **Sauvola** & **Grid-Sauvola** & **Grid-Kittler** & **GLGM** & **Grid-FADIT** \\ \hline Image 1 & 7.3912 & 10.6198 & 8.1273 & 9.4828 & 13.0383 \\ Image 2 & 4.6402 & 16.4017 & 20.4056 & 19.9702 & 20.5779 \\ Image 3 & 3.6341 & 14.0252 & 6.2377 & 15.6426 & 17.6719 \\ Image 4 & 4.7587 & 14.4093 & 13.6244 & 14.0242 & 16.6563 \\ Image 5 & 4.7563 & 17.4495 & 20.0865 & 15.0982 & 21.5843 \\ \hline \hline \multicolumn{5}{c}{**Misclassification Error**} \\ \hline  & **Sauvola** & **Grid-Sauvola** & **Grid-Kittler** & **GLGM** & **Grid-FADIT** \\ \hline Image 1 & 0.1823 & 0.0867 & 0.1539 & 0.1126 & 0.0497 \\ Image 2 & 0.3435 & 0.0229 & 0.0091 & 0.0101 & 0.0088 \\ Image 3 & 0.4331 & 0.0396 & 0.2378 & 0.0273 & 0.0171 \\ Image 4 & 0.3343 & 0.0362 & 0.0434 & 0.0396 & 0.0216 \\ Image 5 & 0.3345 & 0.0180 & 0.0098 & 0.0309 & 0.0069 \\ \hline \hline \multicolumn{5}{c}{**Running Time in MATLAB (Second)**} \\ \hline  & **[PERSON]** & **Grid-Sauvola** & **Grid-Kittler** & **GLGM** & **Grid-FADIT** \\ \hline Image 1 & 38.5553 & 3.7726 & 1.8631 & 0.7000 & 1.8401 \\ Image 2 & 32.3430 & 3.2322 & 1.6674 & 0.5537 & 1.5850 \\ Image 3 & 75.5198 & 7.4040 & 3.6795 & 1.3186 & 3.6751 \\ Image 4 & 87.3679 & 8.4955 & 4.2793 & 1.5257 & 4.2375 \\ Image 5 & 75.0670 & 7.3851 & 3.6802 & 1.3247 & 3.6703 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Objective performance of different methods.