## 4 Discussion and Analysis of the Results
### Analysis of the Dataset and Results  
In this section, a detailed analysis and description of the dataset are presented, and then the results obtained during experimentation will be presented. As depicted in Figure 5, we observed that some of the printed text-line images are not properly aligned with the ground truth due to the occurrence of extra blank spaces between words and/or the merging together of more words during printing. In addition, with the synthetic text-line images, a character at the beginning and/or at the end of the word is missed, which results in misalignment with the ground-truth. To improve the recognition performance, it is important to annotate the data manually or to use better data annotation tools.  
Of several factors, samples with wrongly annotated Amharic text-line images and characters, depicted in Figure 5, are the majors factors causing recognition errors. In general, the recognition errors may occur due to misspelled characters, spurious symbols, or lost characters.  
The proposed model works better on the synthetic test datasets generated with the Visual Geez font type, compared to the character error rate observed on the Power Geez font type and the printed test data. The generalization of this model, especially on synthetic dataset generated with the Power  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Type** & **Font Type** & **Test Data Size** & **CER (\%)** \\ \hline Printed & Power Geez & 2907 & 1.59 \\ \hline Synthetic & Visual Geez & 6479 & 1.05 \\  & Power Geez & 9245 & 3.73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results (Character Error Rate (CER)).  
Figure 5: Samples with wrongly annotated images and GT from the test dataset. (**a**) Synthetic text-line image; the word marked with yellow rectangle is a sample mislabeled word where the first character (\(\mathbf{\tau}\)) in the GT, marked with a red circle, is missed in the input image but it exists in GT. (**b**) Printed text-line image; a punctuation mark called a full stop/period, bounded with a purple rectangle in the input image, is incorrectly labeled as two other punctuation marks called word separators, indicated by the green and red text colors in the GT.  
Geez font type, is not as good as the Visual Geez one. This happens mainly because of the significantly larger number of text-line images in the test set and the nature of the training samples (i.e., the text-line images and the ground truth are not properly annotated due to the existence of deformed characters and missing characters in the beginning and/or end of the text-line images during data generation but not in the ground truth). In addition, text-line images generated with the Power Geez font are relatively blurred, resulting in poor recognition accuracy.  
In Figure 6, sample Amharic text-line images and predicted texts containing different types of character errors during testing are shown.  
For example, as illustrated at the top of Figure 5a, the character (**1**) marked by the green rectangle in the input image is substituted by the character (**7**) in the predicted text. On the other hand, the two characters (**7** and **n**) marked with the red rectangle are deleted characters. Other character errors are depicted at the top of Figure 6b; characters (**4** and **7**) from the input image, marked with the red rectangles, are deleted characters, while characters (**4** and **7**) in the predicted text, written with a blue color, are inserted characters.