## 5 Comparison and Discussion  
To evaluate the performances of all the investigated approaches, we employed accuracy, as used in the baseline study [12], allowing for a direct comparison of the results. Beyond this metric, which shows the overall accuracy of the model, we also calculated the accuracy for each individual kanji character to identify potential patterns or anomalies in the models' performance. Figures 7-9 provide a comparison of all the examined approaches alongside the baseline method from [12] across all three cases:  
* Classification task for 150 classes (Figure 7);
* Classification task for 200 classes (Figure 8);
* Classification task for 300 classes (Figure 9). These figures visually confirm the superior accuracy of Algorithm 2 compared to those of all the other methods. The analysis of the training curves across all the Kuzushij-Kanji fine-tuning experiments (Appendix A, Figures A2-A13) reveals consistent convergence behaviors. The approaches involving full network training (Vanilla, Vanilla + Aug, and Algorithm 2) typically displayed rapid performance gains within the initial 2-4 epochs, followed by a period of gradual fine-tuning. This indicates that the models quickly learned the foundational features relevant to the target task. Algorithm 2, benefiting from relevant pretrained weights, consistently converged to lower loss values and higher final accuracies compared to those of the Vanilla models trained from scratch, suggesting that it reached a more optimal local minimum in the loss landscape. Algorithm 1 also converged quickly but plateaued earlier at significantly higher loss and lower accuracy levels, highlighting the limitations of only fine-tuning the final layer for this complex task. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Number of Classes** & **Accuracy** & **95\% Confidence Interval** \\ \hline CNN ensemble & & & \\ (previous SOTA) & 150 & 96.43\% & (96.18–96.68\%) \\ Algorithm 2 & 150 & 97.94\% & (97.75–98.13\%) \\ Algorithm 2 & 200 & 97.92\% & (97.74–98.10\%) \\ Algorithm 2 & 300 & 97.62\% & (97.44–97.80\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confidence intervals for accuracy measurements. A critical tradeoff exists between the fine-tuning strategies concerning the computational efficiency and performance. Algorithm 1 (freezing convolutional layers) offered substantially reduced training times and computational demands (memory and processing power) per epoch, as the gradients only needed to be computed and applied to the final classification layer. This makes it a potentially attractive option for resource-constrained environments or rapid prototyping. However, as demonstrated by the results (Tables 5-7), this efficiency was achieved at the significant cost of lower recognition accuracies across all the class counts. Conversely, Algorithm 2, requiring gradient computation and weight updates throughout the entire network, demanded considerably more computational resources and longer training durations. Yet, this investment consistently yielded statistically significant  
Figure 8: Comparison of all the evaluated models for the 200-class classification task. Figure 7: Comparison of all the evaluated models with the baseline method from [12] for the 150-class classification task. improvements and the overall best performance. The Vanilla models, also requiring full network training, represent an intermediate computational load but generally needed more epochs to reach their optimal performances compared to that of the pretrained Algorithm 2, which started closer to a good solution. It is important to note that the baseline solution from [12] was designed specifically for the recognition of 150 classes; therefore, its results are not included in Figures 8 and 9. The process for obtaining the initial weights through training in the CASIA-HWDB dataset yielded surprisingly strong results.