## 4 Results and Discussion
### Performance Evaluation
#### 4.4.2 Detection Rate (DR), Recognition Accuracy (RA) and F-Measure (FM)  
The Detection Rate (DR) is the ratio of the number of correctly detected lines to the given number of ground truth lines in the handwritten image document and is computed as in Equation (18). \[\text{Detection Rate, }\text{DR}=\frac{N_{c}}{N_{g}} \tag{18}\]  
where \(N_{c}\) is the number of correctly detected lines and \(N_{g}\) is the number of ground truth lines. The detection rate for the experiment is found to be 85.5%. The accuracy of the detected lines is calculated using the recognition accuracy metric as in Equation (19):  
\[\text{Recognition Accuracy, }\text{RA}=\frac{N_{C}}{N_{d}} \tag{19}\]  
where \(N_{d}\) is the number of detected lines. The implementation of the proposed method results in recognition accuracy of 99.39%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Text Line** & **No. of Lines** & **No. of Correctly Segmented Lines** & **Accuracy (\%)** \\ \hline Text lines & 7535 & 6443 & 85.507 \\ \hline Overlapping lines & 629 & 441 & 70.11 \\ \hline Short lines & 2607 & 2577 & 98.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of the proposed method in segmenting text lines, overlapping lines and short lines. The harmonic mean of DR and RA is called the F-measure (FM) and it is computed using Equation (20). \[\text{F-measure, FM}=\frac{2\times\text{DR}\times\text{RA}}{\text{DR}+\text{RA}} \tag{20}\]  
The value of FM lies between 0 and 1. If FM is 1, it means that DR and RA are the maximum. For the LIPI database, the proposed method results in an F-measure of 91.92%. If DR or RA is zero, then FM is zero. The observed values of DR, RA and FM for the experiment in percentages are given in Table 3.  
The proposed method is compared with language-independent text line extraction algorithms like A* Path Planning [33] and the piecewise painting algorithm [34]. The accuracy of these text line extraction algorithms on the newly developed Malayalam handwritten document image database LIPI is displayed in Table 4. In total, 378 document images from the LIPI database are selected to perform the experiment. The proposed method can extract 4912 text lines perfectly, out of 5599, whereas the result is 3258 and 1495 text lines for the A* Path Planning and piecewise painting algorithms. The proposed method shows higher accuracy of 87.7% compared to 58.19% and 26.7% for A* Path Planning and the piecewise painting algorithm on the LIPI database. From the experiments conducted, it is observed that the piecewise painting algorithm incorrectly segments ascenders and descenders in the Malayalam language, the character 'Chandrakkala' and overlapping lines. This is the reason for its low accuracy of 26.7%. An interesting observation is that the A* Path Planning algorithm segments the text lines containing the character 'Chandrakkala' and the ascenders and descenders almost correctly. However, it fails to segment some of the text lines. Therefore, the accuracy is only 58.19%. To perform the comparison, the database used is the newly created Malayalam handwritten document image database, LIPI. Since the proposed method is specific to the Malayalam language, it will not show good performance for databases of other languages. A database for Malayalam handwritten document images is not publicly available. Therefore, a comparison with another database in the Malayalam language is also not possible. However, the proposed technique is expected to perform well even if new Malayalam handwritten document images are added to the dataset. The detection rate, recognition accuracy and F-measure of the proposed method, A* Path Planning and piecewise painting algorithm are plotted in Figures 20-22, respectively.