论文标题
对历史报纸的页面细分的DNN体系结构的评估
An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers
论文作者
论文摘要
在具有复杂布局的历史文档（例如报纸）的光学特征识别（OCR）中，一个重要的，特别具有挑战性的步骤是将文本与非文本内容（例如页面边框或插图）分开。此步骤通常称为页面细分。尽管已经提出了各种基于规则的算法，但最近深层神经网络（DNN）的适用性最近引起了很多关注。在本文中，我们对11种不同的DNN骨干架构以及9种不同的瓷砖和缩放配置进行系统评估，以分离文本，表或表列线。我们还显示了标签数量和培训页面数量对分割质量的影响，我们使用Matthews相关系数进行了测量。我们的结果表明，（取决于任务）Inception-Resnet-V2和EfficityNet骨架最有效，垂直瓷砖通常比其他瓷砖方法更可取，并且构成30至40页的培训数据大多数时候就足够了。
One important and particularly challenging step in the optical character recognition (OCR) of historical documents with complex layouts, such as newspapers, is the separation of text from non-text content (e.g. page borders or illustrations). This step is commonly referred to as page segmentation. While various rule-based algorithms have been proposed, the applicability of Deep Neural Networks (DNNs) for this task recently has gained a lot of attention. In this paper, we perform a systematic evaluation of 11 different published DNN backbone architectures and 9 different tiling and scaling configurations for separating text, tables or table column lines. We also show the influence of the number of labels and the number of training pages on the segmentation quality, which we measure using the Matthews Correlation Coefficient. Our results show that (depending on the task) Inception-ResNet-v2 and EfficientNet backbones work best, vertical tiling is generally preferable to other tiling approaches, and training data that comprises 30 to 40 pages will be sufficient most of the time.
