Writer identification based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNet has two pathways: feature pyramid which is used to extract feature maps and fragment pathway which is trained to predict the writer identity based on fragments extracted from the input image and the feature maps on the feature pyramid. We conduct experiments on four benchmark datasets, which show that our proposed method can generate efficient and robust deep representations for writer identification based on both word and page images.

中文翻译：

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FragNet：使用深度片段网络进行作者识别


基于少量文本的作者识别是一个具有挑战性的问题。在本文中，我们提出了一种基于大约包含一个单词的单词或文本块图像的作者识别的新基准研究。为了在这些文字图像上提取强大的特征，提出了一种名为 FragNet 的深度神经网络。 FragNet 有两个路径：特征金字塔，用于提取特征图；片段路径，用于根据从输入图像中提取的片段和特征金字塔上的特征图来预测作者身份。我们在四个基准数据集上进行了实验，结果表明我们提出的方法可以为基于单词和页面图像的作者识别生成高效且鲁棒的深度表示。