## 4 Results  
During the study, the data used were divided into two groups: training and test in a ratio of 2:1. In a smaller database that means the training group contains over 20,000 feature vectors, and the test group contains over 10,000 feature vectors, which contain Hiragana and Katakana related data only. The first step was to check the classification results using standard classifiers (Table 1). Among the classification algorithms used, the highest accuracy was obtained using the Support Vector Machine, which was 92.517%. The lowest was obtained using the Gaussian Naive Bayes classifier (58.783%). The next step was to create two multimodal classifiers. The first one was based on all five classifiers mentioned in the earlier experiment. The second one used only three classifiers: the Support Vector Machine, the Decision Tree Classifier and the Gaussian Naive Bayes Classifier. These classifiers were selected on the basis of the research conducted. This composition resulted in the highest combined score. The obtained results for the five-modal classifier are higher than the three-modal classifier (Table 2). Results obtained with both trimodal and five-modal classifier are lower than those achieved by using Support Vector Machine. For this reason, it can be assumed that the use of multi-modular classifiers to classify characters using the authors' methods is unfounded. In the case of a tie for multimodal classifiers, the score taken into account is that obtained by the classifier with the highest level of accuracy. Additionally, it was tested whether it is possible to reduce the number of parameters without affecting the quality of classification (Figure 9). From the graph shown, it is possible to reduce the number of parameters to 65 without losing accuracy in the results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Accuracy** \\ \hline Decision tree (max\_depth = 15) & 77.630\% \\ k-NN (k = 11) & 85.712\% \\ SVM (kernel = rbf) & 92.517\% \\ Logistical Regression (solver = ’liblinear’, max\_iter = 150) & 89.834\% \\ Gaussian Naive Bayes classifier (smoothing = 1 \(\times\) 10\({}^{-9}\)) & 58.783\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classificators comparison. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Accuracy** \\ \hline Trimodal classifier & 86.574\% \\ Five-modal classifier & 91.435\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classificators comparison. For the second database, the results obtained using standard classifications are much lower. The obtained results did not exceed 62% accuracy (Table 3). The authors conducted a study to select the best parameters. The parameters presented were experimentally selected. A sequential neural network consisting of one input layer, three hidden layers and one output layer was used to obtain better results. In this way, a result of 99.934% accuracy was obtained. In addition, it was checked whether reducing the number of parameters in the feature vector would somehow affect the obtained result (Figure 10). In this case, optimal results can already be obtained when limiting the number of features to 40. The result presented is one of the highest among the compared algorithms (Table 4). The last column shows the obtained result declared in the mentioned works. Comparing results between authors is difficult due to the use of different databases, but the closest comparison seems to be with authors using the ET19B database. In this database, the images are already pre-segmented and binarised. The work presented by the authors shows one of the highest results achieved for the ET19 database and this result is 99.934%.