论文标题
图形卷积网络统一线和段落检测
Unified Line and Paragraph Detection by Graph Convolutional Networks
论文作者
论文摘要
我们将文档中的线和段落检测到统一的两级聚类问题的任务。给定一组与单词大致相对应的文本检测框，文本行是一组框，段落是一组线。这些簇形成了一个两级树，代表文档布局的主要部分。我们使用图形卷积网络来预测文本检测框之间的关系，然后从这些预测中构建两个级别的群集。在实验上，我们证明了统一的方法可以非常有效，同时仍可以在检测公共基准和现实世界图像中检测段落的最先进质量。
We formulate the task of detecting lines and paragraphs in a document into a unified two-level clustering problem. Given a set of text detection boxes that roughly correspond to words, a text line is a cluster of boxes and a paragraph is a cluster of lines. These clusters form a two-level tree that represents a major part of the layout of a document. We use a graph convolutional network to predict the relations between text detection boxes and then build both levels of clusters from these predictions. Experimentally, we demonstrate that the unified approach can be highly efficient while still achieving state-of-the-art quality for detecting paragraphs in public benchmarks and real-world images.
