## 5 Results and Analysis
This average refers to the proportion of each classes' support, relative to the sum of all support values. In our case of multiclass classification, using Equation (6), the calculated values of weighted \(F1\) score are 0.997 and 0.996 for model M2 and model K5, respectively. Micro averaging computes a universal \begin{table}
\begin{tabular}{c c c|c c c} \hline \hline
**Letters** & **Precision** & **Recall** & **F1 score** & **Support** & **Support Proportion** \\
**(A–Z)** & **/Class** & **/Class** & **/Class** & **/Class** & **/Class** \\ \hline class A & 0.99 & 0.99 & 0.99 & 1459 & 0.02 \\ class B & 1.00 & 0.99 & 1.00 & 4747 & 0.064 \\ class C & 0.99 & 1.00 & 0.99 & 2310 & 0.031 \\ class D & 1.00 & 1.00 & 1.00 & 5963 & 0.08 \\ class E & 0.99 & 0.99 & 0.99 & 1986 & 0.027 \\ class F & 0.99 & 0.99 & 0.99 & 1161 & 0.016 \\ class G & 1.00 & 0.99 & 0.99 & 1712 & 0.023 \\ class H & 0.99 & 1.00 & 0.99 & 2291 & 0.031 \\ class I & 1.00 & 1.00 & 1.00 & 3894 & 0.052 \\ class J & 0.99 & 1.00 & 0.99 & 2724 & 0.037 \\ class K & 0.99 & 0.99 & 0.99 & 2315 & 0.031 \\ class L & 0.98 & 0.99 & 0.99 & 1109 & 0.015 \\ class M & 1.00 & 0.99 & 1.00 & 3841 & 0.052 \\ class N & 1.00 & 1.00 & 1.00 & 11,524 & 0.155 \\ class O & 0.99 & 0.99 & 0.99 & 2488 & 0.033 \\ class P & 0.99 & 0.99 & 0.99 & 1235 & 0.017 \\ class Q & 1.00 & 1.00 & 1.00 & 4518 & 0.061 \\ class R & 0.99 & 0.99 & 0.99 & 1226 & 0.016 \\ class S & 1.00 & 0.98 & 0.99 & 229 & 0.003 \\ class T & 1.00 & 0.99 & 0.99 & 870 & 0.012 \\ class U & 1.00 & 0.99 & 1.00 & 2045 & 0.027 \\ class V & 1.00 & 1.00 & 1.00 & 9529 & 0.128 \\ class W & 0.99 & 0.98 & 0.99 & 1145 & 0.015 \\ class X & 0.99 & 0.99 & 0.99 & 2165 & 0.029 \\ class Y & 0.97 & 0.96 & 0.97 & 249 & 0.003 \\ class Z & 0.99 & 0.99 & 0.99 & 1755 & 0.024 \\ \hline \multicolumn{5}{c}{Total} & 74,490 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification report of model K5 for A–Z recognition. average \(F1\) score by taking the sums of the TTP, TFN, and TFP. Micro \(F1\) score is computed using Equation (7), which is derived from Equation (5). Table 5 shows the micro, macro, and weighted average of \(F1\) score for the best two proposed models (model M2 and model K5). The performance of a classification model is evaluated by the following well-accepted F-measurement matrix:  
\[\textit{Weighted }F1\textit{ score}=\sum_{a}^{b}(\textit{Per class }F1\textit{ score}*\textit{Support Proportion}) \tag{6}\]  
\[\textit{Micro }F1\textit{ score}=\frac{TP}{TP+\frac{1}{2}\left(FP+FN\right)} \tag{7}\]  
Micro \(F1\) score works well with the balanced dataset. In this project, as the datasets are imbalanced, the macro average would be an ideal choice where all classes are equally significant, because it treats all classes equally. The weighted average is preferred if we want to assign greater contribution to classes with more examples, because each classes' contribution to the \(F1\) average is weighted by its size. We obtained good results with multiclass classification. The proposed CNN model (with four convolutional layers, three max-pooling layers, and two dense layers) for handwritten recognition was approached by keeping in mind that it should not start overfitting. However, this work shows how different learning rates and optimizers play a part in the models' performances. Additionally, classification reports are presented for micro, macro, and weighted averages. A comparative analysis of how the best two proposed models perform with other distinguished models in recent years by different researchers is shown in Table 6. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**F-Measure**} & **Model M2** & **Model K5** \\  & **(Digit Recognition)** & **(Letter Recognition)** \\ \hline Micro \(F1\) score & 0.996 & 0.995 \\ Macro \(F1\) score & 0.998 & 0.992 \\ Weighted \(F1\) score & 0.997 & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Micro, macro, and weighted average of \(F1\) score for the best two proposed models.