## 6 Conclusions  
This study aimed to develop an effective machine-learning approach for recognizing handwritten Japanese kanji. The proposed methodology involved implementing the Preact ResNet-18 model for kanji recognition, both with and without data augmentation, to evaluate its effectiveness and the impact of the augmentation on the recognition performance. Furthermore, a novel model based on cross-language transfer learning was developed using the Preact ResNet-18 architecture. The model was initially trained in a Chinese dataset and then fine-tuned in a Japanese dataset to improve its kanji recognition accuracy. Experiments were conducted for 150, 200, and 300 kanji classes, with evaluations based on key performance metrics, such as the recognition accuracy rate, mAP, and mAA. The results indicated that although data augmentation provided slight benefits in certain scenarios, particularly with larger class sets, it did not consistently enhance the recognition performance. This suggests that the primary variations in handwritten kanji lie less in simple geometric transformations (e.g., rotation and minor scaling/translation) and more in intrinsic aspects (e.g., stroke thickness, connection variations, and character-specific deformation) not fully captured by the applied data augmentation techniques. Several factors, such as stroke thickness variation, slight deviations in stroke order or connection, and character-specific deformations, might be more critical for robust recognition. The applied affine and cropping transforms might not fully represent these more intrinsic handwriting variations. Therefore, we suggest further experimentation with alternative augmentation strategies. Additionally, models trained from scratch underperformed compared to their pretrained counterparts, reinforcing the effectiveness of transfer learning in handling high-class-count classification tasks. This study also highlighted the importance of fine-tuning strategies. Unfreezing all the layers during training yielded the best performance, whereas restricting training to only the final layer yielded suboptimal results. This finding aligns with the notion that deep feature representations learned during pretraining must be fully adapted to the new task for the optimal performance. Moreover, pretraining in a large, well-balanced dataset, such as CASIA-HWDB, proved to be highly beneficial. The model trained with Algorithm 2 consistently outperformed the other approaches, achieving state-of-the-art results, with a statistically significant 1.51% accuracy improvement for recognizing 150 kanji and a 1.19% improvement for recognizing 300 kanji over the previous benchmarks. These findings validate the hypothesis that leveraging visually similar handwritten Chinese characters provides a strong foundation for kanji recognition. Moreover, statistical analysis confirms that these improvements are statistically significant (\(p<0.001\) and non-overlapping confidence intervals), providing strong evidence for the effectiveness of our cross-language transfer-learning approach. Overall, this research demonstrates that combining robust pretraining with appropriate fine-tuning strategies can significantly enhance kanji recognition models. The insights gained not only advance the state of handwritten kanji recognition but also offer valuable benchmarks and methodologies for future studies in multiclass classification. Future research will focus on further improving the best-performing model by refining metric scores and expanding the range of recognizable kanji. This will involve experimenting with alternative architectures and enhancing data preprocessing techniques, especially because data preprocessing is critical in computer vision tasks, as demonstrated by [39]. Additionally, specific recognition challenges, such as the poor differentiation of visually similar kanji (e.g., \(\Re\) and \(\Re\) (see Appendix B for details)) and unexpected underperformance for simpler characters, will be investigated further.