## 4 Results and Discussions
### Discussions
#### 4.2.3 Character-Wise Discussion  
Since Dataset 15 was the best performer, we considered it for the character-wise analysis of the model. Observation of Dataset 15 in Tables 7, 9 and 12 reveals that the MLP classifier recognized the Devnagari characters \(\overline{\pi}\) (Ka), \(\overline{\pi}\) (Cha), \(\overline{\pi}\) (Jha), \(\overline{\pi}\) (Thha), \(\overline{\pi}\) (Adna), and \(\overline{\pi}\) (Pha) excellently, as the precision, recall, and F1 score values for these characters are almost equal to 1 (100%); this might be due to the uniqueness of their shapes. For the same reason, the SVM classifier produced fine results for the letters \(\overline{\pi}\) (ka), \(\overline{\pi}\) (Cha), \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) (Adna), \(\overline{\pi}\) (Pha), and \(\overline{\pi}\) (La) with respective precision, recall, and F1 score values as 1. It is interesting to know that both the classifiers responded in an excellent manner to the characters \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) ([PERSON]), and \(\overline{\pi}\) ([PERSON]). This gives a clear indication of uniqueness in the shape of these characters. The MLP classifier scored satisfactory but had a comparatively low F1 score (refer to Table 9) for the character class \(\overline{\pi}\) (Ya). The same is true with the SVM classifier (refer to Table 12) for character classes \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) ([PERSON]), \(\overline{\pi}\) ([PERSON]), and \(\overline{\pi}\) ([PERSON]). This might be due to the resemblance of their shapes with some other characters of the complete dataset. For precise result-analysis of the proposed model, the Results section incorporated individual character-class-wise scores of various performance metrics for the proposed datasets and the proposed classifiers (refer to Tables 7 and 9-12). To relate different values of precision, recall, and F1 score with their practical aspect, let us take one example. Referring to Table 7, for Dataset 15, the precision value of character \(\overline{\pi}\) is 0.96 i.e., 96%, which shows that 96% of the total predictions made for the character were correct and for the remaining 4% prediction, the other characters were falsely classified as character \(\overline{\pi}\) (Ya). This can also be verified from the confusion matrix given in Figure 8a; on the axis of \"Predicted value\", the total 503 predictions can be observed in the column of character \(\overline{\mathfrak{q}}\) (class no. 25), out of which 483 were correctly predicted as \(\overline{\mathfrak{q}}\); and some other characters such as \(\overline{\mathfrak{q}}\) (class no. 2), \(\overline{\mathfrak{q}}\) (class no. 3), \(\overline{\mathfrak{q}}\) (class no. 5), \(\overline{\mathfrak{q}}\) (class no. 12), \(\overline{\mathfrak{q}}\) (class no. 16), \(\overline{\mathfrak{q}}\) (class no. 20), \(\overline{\mathfrak{q}}\) (class no. 28), and \(\overline{\mathfrak{q}}\) (class no. 31) were falsely classified as character \(\overline{\mathfrak{q}}\) for 3, 4, 1, 1, 6, 2, 1, 1, and 1 times, respectively, i.e., total 20 false-positive predictions. It is interesting to know that most of the false predictions were made by character \(\overline{\mathfrak{q}}\) (class no. 16), which has a shape resembling the character under test i.e., character \(\overline{\mathfrak{q}}\) (Ya). The higher the precision value, the lower were the false-positive predictions. Referring to Table 12, for Dataset 15, the recall value of character \(\overline{\mathfrak{q}}\) is 0.97 i.e., 97%, which shows that 97% of the total test samples of the character were correctly predicted and for the remaining 3% samples, the character \(\overline{\mathfrak{q}}\) was falsely classified as some other character.