## 4 Results and Discussion
### Implementation Results  
The image obtained from the scanner is a color image, as in Figure 4. It is converted to grayscale and then to binary. The binary image is inverted to make the background black and foreground (handwritten characters) white. The binary image obtained after such processing is shown in Figure 5. The binary image is divided into three vertical stripes so as to have almost straight lines, as illustrated in Figure 7. In the proposed method, the number of vertical stripes is fixed at three. If the page is not divided into vertical stripes, then the text lines will be slanting. A similar case arises when the number of vertical stripes is 2. In both these cases, text line extraction based on the horizontal projection method will not yield good accuracy. If the number of vertical stripes is increased above three, the width of each vertical stripe decreases accordingly. Then, there is a possibility of missing the portion of the text line present in these vertical stripes completely, which is against the assumptions based on which the algorithms are designed. The text lines extracted from vertical stripes using the horizontal projection (HP) method consist of overlapping and short lines, as shown in Figure 8. Multiple lines or overlapping lines are present in vertical stripe 1 (\(VS_{1}\)) and vertical stripe 2 (\(VS_{2}\)) in lines 12 and 13, respectively. The average height of the characters for a document in the  
Figure 6: (**a**) Ground truth of the first text line in the image shown in Figure 5; (**b**) ground truth of the fifth text line in the image shown in Figure 5.  
Figure 7: Binarized handwritten document image divided into three vertical stripes. database images is between 11 and 23 and is dependent on the handwriting of the different authors. Based on the average height of the characters, the threshold \(T_{ox}\), computed using Equations (1) and (2) to detect overlapping lines, is in the range of 91.5 to 140.65 for all the images in the database. This is because of the size variations in the characters written by different authors. To demonstrate the segmentation of overlapping lines using the proposed technique, such lines in \(VS_{2}\) in Figure 8 are shown separately in Figure 9. It is observed that the HP value is non-zero between the overlapping lines because of ascenders and descenders in the language. To segment the overlapping lines, a region is found using the proposed method and it is shown using dotted lines for a better understanding in Figure 9. The overlapping lines are segmented correctly by applying the proposed techniques discussed in Sections 3.2 and 3.3 and is shown in Figure 10. For the overlapping lines shown in Figure 9 of size 162 \(\times\) 552, the region for the segmentation of the lines is found by computing the values \(r_{1},\ r_{2}\) using Equations (7)-(10) and is obtained as 63.5 and 72.5, respectively. \(r_{1}\) and \(r_{2}\) are marked manually using dotted lines in Figure 5.0 Rows 64 to 72 represent the character region, as discussed in Section 3.3. Therefore, rows 1 to 63 are the upper region, and rows 73 to 162 are the lower region. From the database of 402 handwritten document images, 629 overlapping lines are detected, of which 441 are segmented correctly. The overlapping lines in \(VS_{1}\) and \(VS_{2}\) in Figure 8 are segmented correctly and the result of the segmentation is depicted in Figure 5.1  
The remaining short lines in Figure 11 are addressed using the proposed methods discussed in Sections 3.4 and 3.5. The height of the short lines is very small and the characters present in such lines can be as small as a single dot. The short lines are joined perfectly to the appropriate lines, as shown in Figure 5.2 The thresholds \(t_{sh_{1}}\) and \(t_{sh_{2}}\), computed according to the proposed method in Section 3.4 to detect short lines, are in the range of 19 to 39.9 and 2.7145 to 13.3, respectively. These two thresholds are dependent on the average character width per page, which lies between 10 and 22 for the document images in the database.