This work presents a two-stage text line detection method for historical
documents. Each detected text line is represented by its baseline. In a first
stage, a deep neural network called ARU-Net labels pixels to belong to one of
the three classes: baseline, separator or other. The separator class marks
beginning and end of each text line. The ARU-Net is trainable from scratch with
manageably few manually annotated example images (less than 50). This is
achieved by utilizing data augmentation strategies. The network predictions are
used as input for the second stage which performs a bottom-up clustering to
build baselines. The developed method is capable of handling complex layouts as
well as curved and arbitrarily oriented text lines. It substantially
outperforms current state-of-the-art approaches. For example, for the complex
track of the cBAD: ICDAR2017 Competition on Baseline Detection the F-value is
increased from 0.859 to 0.922. The framework to train and run the ARU-Net is
open source.

本文提出了一种用于历史文献的文本行检测方法，采用了两种阶段的方法，第一阶段使用了一个名为 ARU-Net 的深度神经网络进行像素的分类，第二阶段则采用自底向上的聚类算法进行 Baseline 的建立，该方法在复杂布局、曲线和任意方向文本行方面具有出色的性能和表现。