Cross-Language Transfer-Learning Approach via a Pretrained Preact ResNet-18 Architecture for Improving Kanji Recognition Accuracy and Enhancing a Number of Recognizable Kanji

Many people admire the Japanese language and culture, but mastering the language’s writing system, particularly handwritten kanji, presents a significant challenge. Furthermore, translating historical manuscripts containing archaic or rare kanji requires specialized expertise. To address this, we designed a new model for handwritten kanji recognition based on the concept of cross-language transfer learning using a Preact ResNet-18 architecture. The model was pretrained in a Chinese dataset and subsequently fine-tuned in a Japanese dataset. We also adapted and evaluated two fine-tuning strategies: unfreezing only the last layer and unfreezing all the layers during fine-tuning. During the implementation of our training algorithms, we trained a model with the CASIA-HWDB dataset with handwritten Chinese characters and used its weights to initialize models that were fine-tuned with a Kuzushiji-Kanji dataset that consists of Japanese handwritten kanji. We investigated the effectiveness of the developed model when solving a multiclass classification task for three subsets with the one hundred fifty, two hundred, and three hundred most-sampled 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. Our best model achieved 97.94% accuracy for 150 kanji, exceeding the previous SOTA result by 1.51%, while our best model for 300 kanji achieved 97.62% accuracy (exceeding the 150-kanji SOTA accuracy by 1.19% while doubling the class count). This confirms the effectiveness of our proposed model and establishes new benchmarks in handwritten kanji recognition, both in terms of accuracy and the number of recognizable kanji.