## 2 State-of-the-Art Methods  
In this section, we have described the unique challenges of handwritten kanji recognition and the current state of the research in the field. As deep-learning approaches, like ResNet architectures, have shown promising results in various image classification tasks, including medical imaging [1], we will focus on deep-learning methods. The journey began with an exploration of the history of Chinese character recognition [2], where we reviewed early methods and the foundational assumptions that influenced future advancements. This historical perspective highlighted the evolution of techniques and underscored the complexities involved in character recognition. One notable study [3] focused on Chinese character recognition based on similarity, introducing an innovative approach accounting for character clarity/quality. This specificity provided new insights into how subtle visual differences impact recognition accuracy. Another significant contribution was a real-time handwritten kanji recognition system [4], which utilized SVG files and a step-by-step kanji-writing process. This so lution combined a robust model with a tree-structured dictionary for kanji definitions, enabling efficient real-time recognition. Further, we examined a straightforward CNN-based solution for handwritten Chinese character recognition that incorporated a median filter [5] to effectively handle speckled noise and salt-and-pepper noise, common issues in image data. This approach demonstrated the importance of preprocessing in enhancing the model's performance. Addressing the challenge of classifying a large number of classes, we explored the label-mapping (LM) method [6], which outperformed a similar well-known ECOC method [7]. LM improves model predictions in N-class classification tasks, especially when the penultimate layer of the model has fewer dimensions than N-1. This method offers a strategic approach for managing the complexity inherent in multiclass classification. We also reviewed the current state-of-the-art solution in image classification, known as model soups [8]. This technique involves combining several pretrained models, fine-tuned in different datasets or with varying hyperparameter configurations, by averaging their weights. Model soups have demonstrated superior performances, highlighting the potential of ensemble approaches in achieving high accuracy rates in character recognition. The advent of transformers has also spurred innovation in sequence modeling for text recognition, moving beyond traditional recurrent architectures. For example, the PARSeq model [9] leverages features from vision transformers (like ViT or DeiT) but introduces a novel permutation-based autoregressive decoder. This approach allows for the parallel decoding of character sequences, significantly improving efficiency compared to that of purely sequential methods while achieving strong performance in challenging-scene text recognition benchmarks, demonstrating the versatility of transformer components in different sequence-processing paradigms for OCR tasks. Further exploration into transformer variants has also yielded promising results, particularly for complex scripts, like handwritten Chinese characters. For instance, adaptations of the Swin transformer architecture have been proposed to address the challenges specific to offline handwritten Chinese character recognition (HCCR) [10]. One such approach involved simplifying the hierarchical structure of the standard Swin transformer and adjusting window attention mechanisms to create a more parameter-efficient model (S-Swin) specifically tailored for HCCR, achieving competitive recognition accuracy while reducing computational demands compared to those of the original architecture. Another notable contribution is the work by [PERSON] et al. [11], who developed a vision-transformer-based model for plant disease classification.