Paper documents are ideal sources of useful information and have a profound impact on every aspect of human lives. These documents may be printed or handwritten and contain information as combinations of texts, figures, tables, charts, etc. This paper proposes a method to segment text lines from both flatbed scanned/camera-captured heavily warped printed and handwritten documents. This work uses the concept of semantic segmentation with the help of a multi-scale convolutional neural network. The results of line segmentation using the proposed method outperform a number of similar proposals already reported in the literature. The performance and efficacy of the proposed method have been corroborated by the test result on a variety of publicly available datasets, including ICDAR, Alireza, IUPR, cBAD, Tobacco-800, IAM, and our dataset.

纸质文档是有用信息的理想来源，对人类生活的各个方面都具有深远的影响。这些文档可以打印或手写，并包含文本，图形，表格，图表等的组合信息。本文提出了一种从平板扫描/相机捕获的严重变形的打印文档和手写文档中分割文本行的方法。这项工作在多尺度卷积神经网络的帮助下使用了语义分割的概念。使用提出的方法进行线分割的结果优于文献中已经报道的许多类似提议。对各种公开可用的数据集（包括ICDAR，Alireza，IUPR，cBAD，Tobacco-800，IAM和我们的数据集）的测试结果证实了该方法的性能和功效。