# Learning-Free Text Line Segmentation for Historical Handwritten Documents
## 7 Results  
We present results on two recent historical handwritten datasets, DIVA-HisDB and cBAD. For text line detection, we use half of refined-mean for character height estimation, FS1 with a threshold of \(t=1.1\), merge the binarized blob lines, and set the number of knots to \(k=20\) and elongation rate to \(e=2\). For text line extraction, we use Bar-Yosef et al.binarization and split the touching characters among text lines. DIVA-HisDB is a historical document dataset with a high number of consequently touching text lines and heterogeneous in terms of text line height. The input to our algorithm is the ground truth of layout analysis task of the competition. Table 5 shows our results compared to two learning-free algorithms. One is the best performing algorithm in the ICDAR2017 Competition on Layout Analysis for Challenging Medieval Manuscripts [47]. The other [51] is an improved version of the best performing algorithm in the competition. The proposed algorithm can reach a line IU of 100%. The degradation in pixel IU mostly comes from the coarse splitting of touching characters among text lines. [51] performs perfect in terms of the overall Line IU at 100%. The advantage of the proposed algorithm is that it can detect text lines irrespective of touching components across the text lines and is robust to complex layouts. However, its performance is highly dependent on the correct estimation of average character height (Figure 8). Whereas the desired property of the work proposed by [51] is that their results are fairly stable in respect to the varying values of parameters. However, this method is not appropriate for complex layouts with heterogeneous text line lengths or separate text line groups because it labels the text lines with the intuition that all components belonging to the same text line have the same amount of seams below them. cBAD is a historical document dataset with a larger evaluation set and contains documents with varying layouts, originating from different time periods and locations. It is heterogeneous in terms of text line height and length. Input documents are gray-scale images. Some examples of pages with extracted bounding polygons can be seen in Figure 12. Common error cases arise from a group of touching characters that mislead the algorithm to merge the text lines (Figure 12b). Another type of error occurs due to the artifacts in the frame of the page images. We compare our results with the results obtained in cBAD competition. Table 6 shows precision, recall, and F-measure values of the participants. The methods are grouped as learning-based and learning-free. The best performance with an F-measure value of 97.70 is for a learning-based method, DMRZ [9] Two learning-free methods, our method and IRISA [9], achieve close F-measure values. The precision and recall values indicate that the IRISA method splits baselines more precisely but also misses more baselines compared to our method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F-measure** & **Time (m)** \\ \hline Nonsplit & 75.71 & 84.64 & 79.93 & 21.11 \\ Split and Otsu & 81.74 & 92.25 & 86.68 & 23.95 \\ Split and & & & & \\ [PERSON] et al. & 84.17 & 88.68 & 86.45 & 24.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of using different binarization methods, [PERSON] and [PERSON] et al., together with the effect of splitting the touching characters among text lines. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CB55**} & \multicolumn{2}{c}{**CSG18**} & \multicolumn{2}{c}{**CSG636**} & \multicolumn{2}{c}{**Overall**} \\
**Learning-Free** & **Pixel IU** & **Line IU** & **Pixel IU** & **Line IU** & **Pixel IU** & **Line IU** & **Pixel IU** & **Line IU** \\ \hline
[51] & - & - & - & - & - & - & 97.22 & 100.0 \\
[47] & 96.67 & 98.36 & 96.93 & 96.91 & 97.54 & 99.27 & 97.05 & 97.86 \\ Our method & 97.04 & 100 & 98.14 & 98.38 & 97.31 & 100 & 97.50 & 99.46 \\ \hline \hline \end{tabular}
\end{table}