Separating printed or handwritten characters from a noisy background is valuable for many applications including test paper autoscoring. The complex structure of Chinese characters makes it difficult to obtain the goal because of easy loss of fine details and overall structure in reconstructed characters. This paper proposes a method for separating Chinese characters based on generative adversarial network (GAN). We used ESRGAN as the basic network structure and applied dilated convolution and a novel loss function that improve the quality of reconstructed characters. Four popular Chinese fonts (Hei, Song, Kai, and Imitation Song) on real data collection were tested, and the proposed design was compared with other semantic segmentation approaches. The experimental results showed that the proposed method effectively separates Chinese characters from noisy background. In particular, our methods achieve better results in terms of Intersection over Union (IoU) and optical character recognition (OCR) accuracy.

中文翻译：

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使用GAN从嘈杂的背景中分离汉字

将印刷字符或手写字符与嘈杂的背景分开对于许多应用（包括试纸自动打分）非常有价值。汉字结构复杂，容易丢失目标，因为容易丢失重构的汉字的精细细节和整体结构。提出了一种基于生成对抗网络（GAN）的汉字分离方法。我们使用ESRGAN作为基本的网络结构，并应用了扩展卷积和新颖的损失函数，这些函数提高了重构字符的质量。测试了在实际数据收集中使用的四种流行中文字体（Hei，Song，Kai和Imitation Song），并将所提出的设计与其他语义分割方法进行了比较。实验结果表明，该方法有效地将汉字与嘈杂的背景区分开。尤其是，我们的方法在联盟交叉口（IoU）和光学字符识别（OCR）准确性方面取得了更好的结果。