Semantic and Verbatim Word Spotting Using Deep Neural Networks

T. Wilkinson, Anders Brun
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引用次数: 78

Abstract

In the last few years, deep convolutional neural networks have become ubiquitous in computer vision, achieving state-of-the-art results on problems like object detection, semantic segmentation, and image captioning. However, they have not yet been widely investigated in the document analysis community. In this paper, we present a word spotting system based on convolutional neural networks. We train a network to extract a powerful image representation, which we then embed into a word embedding space. This allows us to perform word spotting using both query-by-string and query-by-example in a variety of word embedding spaces, both learned and handcrafted, for verbatim as well as semantic word spotting. Our novel approach is versatile and the evaluation shows that it outperforms the previous state-of-the-art for word spotting on standard datasets.
使用深度神经网络的语义和逐字单词识别
在过去的几年里,深度卷积神经网络在计算机视觉中变得无处不在,在物体检测、语义分割和图像字幕等问题上取得了最先进的结果。然而,它们还没有在文档分析社区中得到广泛的研究。本文提出了一种基于卷积神经网络的单词识别系统。我们训练一个网络来提取一个强大的图像表示,然后将其嵌入到一个词嵌入空间中。这允许我们在各种词嵌入空间(包括学习的和手工制作的)中使用按字符串查询和按示例查询来执行词定位,用于逐字和语义词定位。我们的新方法是通用的,评估表明它在标准数据集上优于以前的最先进的单词识别方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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