## 1 Introduction
The main contributions of this paper can be summarized as follows:  
* We have implemented the Preact ResNet-18 model tailored for handwritten kanji recognition with and without data augmentation and explored the efficiency of this architecture and the data augmentation's impact on the kanji recognition task;
* We have designed a new model for handwritten kanji recognition based on the concept of cross-language transfer learning using a Preact ResNet-18 architecture, where the designed model is originally trained in the Chinese dataset and then fine-tuned in the Japanese dataset;
* We have adapted two training algorithms for the designed model, where the first one is based on the freezing of all the weights during the fine-tuning process, except for the weights of the last, fully connected layer, which leads to reductions in the training time and resource consumption, and the second one is based on the unfreezing of all the weights during the fine-tuning process, which leads to an improvement in the model's performance;
* We investigated the effectiveness of the developed model when solving the multiclass classification task for 150, 200, and 300 classes and showed an improvement in the recognition accuracy and an enhancement in a number of recognizable kanji with the proposed model compared to those of the existing methods. The strategy of employing cross-language transfer learning, specifically by pretraining the model on Chinese characters before fine-tuning it on Japanese kanji, is justified by strong theoretical and historical connections that extend beyond a mere superficial visual resemblance. The fundamental rationale lies in the shared etymological roots and the consequent deep structural congruity between the two writing systems. Elaborating on this historical connection, Japanese kanji are directly derived from Chinese characters through a centuries-long process of adoption and adaptation. This shared lineage means that a vast number of characters bear strong structural relationships. Consequently, the visual representations encode fundamentally similar information regarding character construction. This deep structural congruity is evident in shared core elements. For instance, a significant portion of the radicals is common to both writing systems. Pretraining on Chinese characters, thus, allows the model to learn robust representations of these recurring substructures, which serve as essential building blocks for a large percentage of characters in both languages. Recognizing these shared components is vital for recognizing complex characters. Furthermore, this structural commonality extends to the principles of character construction. Although specific calligraphic styles may exhibit variations, the foundational principles governing the stroke types, their relative positions, and conventional sequences (stroke order) are mostly conserved or systematically related between Chinese characters and kanji. A model pretrained on Chinese characters implicitly learns these low-level features, forming a valuable inductive bias for interpreting kanji strokes. Consequently, by pretraining on Chinese characters, the model develops hierarchical feature extractors attuned not just to generic visual patterns but specifically to the shared strokes, radicals, and compositional rules pertinent to this script family. This learned knowledge provides a significantly more effective initialization for the kanji recognition task, facilitating faster convergence and achieving higher accuracy during the fine-tuning phase by leveraging the deeply ingrained structural commonalities inherited from their shared historical origin. The structure of the paper is as follows: The Section 2 presents the results of a review and a critical analysis of existing works on Chinese and Japanese character recognition. Section 3 presents the mathematical basis of ResNet-18 and Preact ResNet-18, with four training approaches explained.