We propose a high-performance fully convolutional neural network (FCN) for
historical document segmentation that is designed to process a single page in
one step. The advantage of this model beside its speed is its ability to
directly learn from raw pixels instead of using preprocessing steps e. g.
feature computation or superpixel generation. We show that this network yields
better results than existing methods on different public data sets. For
evaluation of this model we introduce a novel metric that is independent of
ambiguous ground truth called Foreground Pixel Accuracy (FgPA). This pixel
based measure only counts foreground pixels in the binarized page, any
background pixel is omitted. The major advantage of this metric is, that it
enables researchers to compare different segmentation methods on their ability
to successfully segment text or pictures and not on their ability to learn and
possibly overfit the peculiarities of an ambiguous hand-made ground truth
segmentation.

本文提出了一种基于像素级别处理的高性能全卷积神经网络，用于历史文献图像分割，并引入了一种独立于模糊真实度量的前景像素准确度评价指标，该指标仅计算二值化页面中的前景像素，从而使研究人员可以比较不同的分割方法在文本或图像分割方面的成功能力。