论文标题
配对的图像到图像翻译，以从手写单词中删除罢工
Paired Image to Image Translation for Strikethrough Removal From Handwritten Words
论文作者
论文摘要
例如，出于遗传批评的目的，抄写了直通，手写的单词，由于叠加的笔触的阻塞性能，可能会对人类和机器构成挑战。本文研究了对图像翻译方法的使用，以删除手写单词中的罢工。检查了四个不同的神经网络架构，从几个简单的卷积层到更深的卷积层，采用了密集的块。从一个合成和一个真正的配对罢工数据集获得的实验结果证实，所提出的配对模型的表现优于基于自行车的技术，而使用少于六分之一的可训练参数。
Transcribing struck-through, handwritten words, for example for the purpose of genetic criticism, can pose a challenge to both humans and machines, due to the obstructive properties of the superimposed strokes. This paper investigates the use of paired image to image translation approaches to remove strikethrough strokes from handwritten words. Four different neural network architectures are examined, ranging from a few simple convolutional layers to deeper ones, employing Dense blocks. Experimental results, obtained from one synthetic and one genuine paired strikethrough dataset, confirm that the proposed paired models outperform the CycleGAN-based state of the art, while using less than a sixth of the trainable parameters.
