论文标题
Fragnet：使用深片段网络的作者识别
FragNet: Writer Identification using Deep Fragment Networks
论文作者
论文摘要
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，我们根据单词或文本块图像提出了一项针对作者识别的新基准研究，该研究大约包含一个单词。为了在这些单词图像上提取强大的功能，提出了一个名为Fragnet的深神经网络。 Fragnet有两种途径：特征金字塔，用于提取特征图和片段途径，该路径经过训练，可以根据从输入图像中提取的片段和特征金字塔上提取的特征图来预测作者身份。我们在四个基准数据集上进行实验，这表明我们提出的方法可以基于单词和页面图像为作者识别生成有效且可靠的深度表示。
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.
