# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 5 Experiments and Results
### Results
#### 5.4.2 Results on the ICFHR2010 Dataset  
Table 6 reports the results of the proposed method and other unsupervised methods. The CUBS [25] method is a learning-free method and similar to the proposed method, as it first detects the blob lines that strike through the text lines. The detection phase uses a steerable directional filter which is adaptive to changes in the global pattern by the use of multiple directions. Whereas the proposed method fails with regard to sudden changes in the global pattern (Figure 11). Such regions are assigned to neither text line cluster nor interline cluster, but somewhere in between the two. This failure can be attributed to the fact that documents with fluctuating text lines or severely skewed and curved lines are outside the network's learned domain. As we can see in Figure 12, the salient features do not encode the text line information as well as what we observed in Section 4.8. It is important to note that the proposed algorithm is successful in managing the changes at the local pattern. The network can simply learn all the local patterns, no matter how diversified the local pattern. For instance, a network trained on two different datasets learns all the local patterns despite the differences among the two datasets. This network can be used to segment text lines of the documents from both datasets (Figure 13).  
Figure 11: We have discussed that the proposed method is successful insofar as the input document contains a globally monotonic pattern. Therefore, as one would expect, the method is not successful in documents with fluctuating text lines or severely skewed and curved text lines. The right document contains severely skewed and curved lines compared with the left one, thus the segmentation failed to segment the lines correctly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **LIU** & **PIU** \\ \hline
**Supervised** & 93.08 & 86.97 \\ Mask-RCNN [15] & 94.52 & 90.01 \\
**Unsupervised** & 90.94 & 83.40 \\ [PERSON] et al. [11] & 98.55 & 88.95 \\ Proposed method & 99.28 & 91.40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: LIU and PIU values on the VML-AHTE dataset.  
Figure 12: Visualizing the salient features of documents with severely skewed and curved text lines, one can see why the method fails to segment the text lines. The network does not learn features that can aid in text line segmentation, rather we can see that it instead learned the orientation of the text.  
Figure 13: Visual results from a network trained on a combination of two datasets, VML-AHTE and ICDAR2017. The top two images show the psuedo-RGB images borrowed from pages of the VML-AHTE and ICDAR2017 (book CB55). The bottom two images visualize the salient features of patches from the two datasets. The network memorizes all the different local patterns, as it clearly identified the text and text lines in both examples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DR** & **RA** & **FM** \\ \hline
**Unsupervised** & & & \\ UTLS [22] & 73.22 & 72.38 & 72.36 \\ CUBS [25] & 97.54 & 97.72 & 97.63 \\ Proposed method & 71.70 & 70.46 & 71.04 \\ \hline input image & Salient features & input image & Salient features \\ \hline
**Figure 12**: Visualizing the salient features of documents with severely skewed and curved text lines, one can see why the method fails to segment the text lines. The network does not learn features that can aid in text line segmentation, rather we can see that it instead learned the orientation of the text.