Abstract
Oracle bone inscription is the earliest writing system in China which contains rich information about the history of Shang dynasty. Automatically recognizing oracle bone characters is of great significance since it could promote the research on history, philology and archaeology. The proposed solutions for oracle bone characters recognition are mainly based on machine learning or deep learning algorithms which rely on a large number of supervised training data. However, the existing dataset suffers from the problem of severe class imbalance. In this work, we propose a CycleGAN-based data augmentation method to overcome the limitation. Via learning the mapping between the glyph images data domain and the real samples data domain, CycleGAN could generate oracle character images of high-quality. The quality is evaluated using the quantitative measure. Totally, 185362 samples are generated which could serve as a complementary to the existing dataset. With these generated samples, the state of the art results of recognition task on OBC306 are improved greatly in terms of mean accuracy and total accuracy.
This work is supported by National Natural Science Foundation of China (No. 62007014), China Post doctoral Science Foundation (No. 2019M652678) and the Fundamental Research Funds for the Central Universities(No. CCNU20ZT019).
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Illustration of samples generated via CycleGAN. The samples in a row belong to the same category.
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Wang, W., Zhang, T., Zhao, Y., Jin, X., Mouchere, H., Yu, X. (2023). Improving Oracle Bone Characters Recognition via A CycleGAN-Based Data Augmentation Method. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_8
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