This paper presents a method for text line segmentation of challenging historical manuscript images. These manuscript images contain narrow interline spaces with touching components, interpenetrating vowel signs and inconsistent font types and sizes. In addition, they contain curved, multi-skewed and multi-directed side note lines within a complex page layout. Therefore, bounding polygon labeling would be very difficult and time consuming. Instead we rely on line masks that connect the components on the same text line. Then these line masks are predicted using a Fully Convolutional Network (FCN). In the literature, FCN has been successfully used for text line segmentation of regular handwritten document images. The present paper shows that FCN is useful with challenging manuscript images as well. Using a new evaluation metric that is sensitive to over segmentation as well as under segmentation, testing results on a publicly available challenging handwritten dataset are comparable with the results of a previous work on the same dataset.

中文翻译：

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使用全卷积网络挑战手写文档图像的文本行分割

本文提出了一种具有挑战性的历史手稿图像的文本行分割方法。这些手稿图像包含带有接触成分的狭窄的行间空间，互穿的元音符号以及不一致的字体类型和大小。此外，它们在复杂的页面布局中包含弯曲，多偏斜和多方向的旁注线。因此，边界多边形标记将非常困难且耗时。相反，我们依赖于在同一文本行上连接组件的行掩码。然后，使用完全卷积网络（FCN）预测这些线掩码。在文献中，FCN已成功用于常规手写文档图像的文本行分割。本文表明，FCN还可用于具有挑战性的手稿图像。