## 5 Analysis
However, because of the typically skewed writing style, even in this particular case backslash lines are less intense than segments in the other directions. The features selected in the AF4 model are particularly interesting. It is relatively clear that no feature is dedicated solely to backslash lines, which is demonstrated in Table 10. Instead, we get feature #1 that seems to address the right side of a small arc. This is reflected in Table 11--digits 8, 6, and 3 exhibited particularly intense occurrences of this feature. While features #2 and #3 seem to reflect vertical and horizontal lines, respectively (digit 1 is strongly correlated with feature #2), the relation is not nearly as straightforward as it was in MF4. Feature #4, related to slash lines, seems to reflect a part of digit 2 especially well. The results of the experiment AF5, which yielded the best autoencoder (Section 4.1) and classification accuracy for highly sparse data (Section 4.3), are less intuitive. The idea of horizontal lines is divided between features #2 and #4. Features #1 and #5 seem to handle both slash/backslash lines and sections of small arcs as well--both these features are important for encoding digit 8. Feature #3 clearly describes some cases of curves that are oriented vertically (digits 2, 8, and 6), but it does not involve straight segments. Digit 1 is described mostly with feature #4. Visual features detected by AF5 model are mostly implicit and difficult to describe semantically. \begin{table}
\begin{tabular}{c c c c c} \hline  & **\#1** & **\#2** & **\#3** & **\#4** \\ \hline
**0** & 4.89 & 5.12 & 3.20 & 5.25 \\ \hline
**1** & 3.90 & 4.19 & 2.14 & 3.63 \\ \hline
**2** & 5.07 & 5.20 & 3.36 & 5.77 \\ \hline
**3** & 5.18 & 5.44 & 3.58 & 5.08 \\ \hline
**4** & 5.03 & 5.72 & 2.89 & 5.07 \\ \hline
**5** & 4.92 & 4.97 & 3.26 & 4.76 \\ \hline
**6** & 5.38 & 5.00 & 3.17 & 4.92 \\ \hline
**7** & 4.78 & 5.14 & 2.88 & 4.33 \\ \hline
**8** & 5.41 & 5.72 & 3.46 & 5.27 \\ \hline
**9** & 5.08 & 5.00 & 3.21 & 4.65 \\ \hline
**Avg.** & 4.95 & 5.14 & 3.10 & 4.85 \\ \hline \end{tabular}
\end{table} 
Table 11: The sums of occurrence intensities (values in the encoding) for each visual feature AF4. It must be emphasized that the results in this table are relative, so they can only be compared to the numbers from the same table. \begin{table}
\begin{tabular}{c c c c c} \hline  & **\#1** & **\#2** & **\#3** & **\#4** \\ \hline
**0** & 1.59 & 1.82 & 1.88 & 1.86  
information on the presence of a selected feature indicates not only the presence of specific digit segments associated with that feature, but it may also be indicative of the absence of other features, as is the case with digit 8 (Figure 8). A similar approach was applied to larger images. In order to include even more information in the presented visualization, images containing both digits and other symbols were used. The results are shown in Figures 11-13. The autoencoders were trained in a digit-specific way, but the test images used in this case contain other symbols as well. The selected non-digit shapes, however, generated visible errors--some segments were erroneously enlarged, merged, broken into pieces or blurred. An additional demonstration of the proposed method using a longer text fragment, which is a \(512\times 128\) scan of a postal address, is presented in Figures 14-16. They depict the encoding contents and decoder outputs for the three proposed models. The number of active elements in the sparse encoding is proportional to the image size, which is visibly larger than in the other examples. Digits are clearly readable in the decoder output, as their size is roughly similar to the MNIST samples. Some of the most significant errors occur for the pairs of letters that are especially close to each other, which are visible in the second line of text. Figure 8: Sample encoding of selected digits performed by MF4 model. Apart from the input and output data and highly-sparse encoding (up to 5 non-zero elements in each encoding matrix), visualizations of single features are presented.