Data augmentation for ancient characters via Blend-Font Net

Xiaolu Ren; Sei-ichiro Kamata

doi:10.1117/12.2599410

30 June 2021 Data augmentation for ancient characters via Blend-Font Net

Xiaolu Ren, Sei-ichiro Kamata

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 1187806 (2021) https://doi.org/10.1117/12.2599410
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Historical documents record a lot of precious information through ancient characters. However, some problems like unbalanced character samples and intra-class multi-modality inside the documents are critical factors that limit the performance of existing character recognition technologies. Therefore, we propose a two-stage font generation model, Blend-Font Net, which use some easy to get modern character datasets to augment ancient character dataset and solve these mentioned problems based on blend-font strategy. The model generates new samples by extracting and modifying the font information from the character image. A font generation model learns the mapping between different fonts in the first stage, and the slightly modified model learns how to generate samples that blend two different fonts in the second stage. Extra samples are generated for balancing historical documents dataset through the proposed model. Experiments show that our results have diverse visual effects and improve the accuracy of the text recognition network. Furthermore, the proposed method shows a broad application prospect in similar works as no font label required and multi-modality problem solved.

Citation Download Citation

Xiaolu Ren and Sei-ichiro Kamata "Data augmentation for ancient characters via Blend-Font Net", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 1187806 (30 June 2021); https://doi.org/10.1117/12.2599410

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