SSGAN: generative adversarial networks for the stroke segmentation of calligraphic characters
SSGAN: generative adversarial networks for the stroke segmentation of calligraphic characters
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DOI：
10.1007/s00371-021-02133-2
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发表时间：
2021-04
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影响因子：
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通讯作者：
Fukun Bi;Jianhong Han;Yumeng Tian;Yanping Wang
中科院分区：
文献类型：
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作者：
Fukun Bi;Jianhong Han;Yumeng Tian;Yanping Wang
目前，中国政府正在鼓励人们学习书法，但目前还没有对结果进行自动评估的方法。书法评价具有挑战性，因为书法字符是以笔画为基本单位组成的复杂图形。因此，不仅要考虑整个汉字的结构，还要考虑每一笔的细节。针对这一问题，我们提出了一种书法图像的笔画自动分割方法，作为后续评测任务的基础。具体地说，我们将笔划分割问题归结为图像到图像的转换问题，提出了笔划分割生成对抗性网络(SSGAN)算法。与现有方法性能不佳或不适合在实际工程中使用不同，该方法可以有效地获得准确的结果。SSGAN能够基于多笔画张量训练策略同时生成书法图像的所有笔画。此外，我们还具体设计了嵌入注意模块的ResU-Net结构，以提高结果的准确性。实验结果表明，该方法优于现有的现有模型。
At present, the Chinese government is encouraging people to learn calligraphy; however, an automatic evaluation method for the results is not available. Calligraphy evaluation is challenging, because calligraphic characters are complex graphics composed of strokes as basic units. Therefore, it is necessary to consider not only the structure of the whole character but also the details of each stroke. To address this problem, we propose an automatic stroke segmentation method for calligraphic images as the foundation for the subsequent evaluation task. Specifically, we treat the stroke segmentation problem as an image to image translation problem and propose the stroke segmentation generative adversarial network (SSGAN) algorithm. Unlike the existing approaches that do not exhibit a satisfactory performance or are not suitable for use in practical projects, the proposed approach can efficiently obtain accurate results. The SSGAN enables the simultaneous generation of all the strokes of a calligraphic image, based on a multistroke tensor training strategy. Moreover, we specifically design a ResU-Net structure with embedded attention modules to enhance the accuracy of the results. The experimental results demonstrate the superiority of the proposed method over the existing state of the art models.