R-PHOC：使用CNN的无分段单词识别
R-PHOC: Segmentation-Free Word Spotting using CNN
摘要：
本文提出了一种基于区域的卷积神经网络用于无分割词识别。我们的网络需要作为输入的图像和一组单词候选边界框，并将所有边界框嵌入到嵌入空间中，其中单词定位可以被转换为查询表示和每个候选边界框之间的简单最近邻搜索。我们利用PHOC嵌入，因为它以前已经取得了显着的成功，基于分割的词发现。候选词是使用一个简单的过程，基于使用一些空间约束的分组连接组件。实验表明，直接对图像进行操作的R-PHOC可以改善标准GW数据集的当前最新技术水平，并且在某些情况下与PHOCNET一样好。
英文摘要：
This paper proposes a region based convolutional neural network for segmentation-free word spotting. Our net- work takes as input an image and a set of word candidate bound- ing boxes and embeds all bounding boxes into an embedding space, where word spotting can be casted as a simple nearest neighbour search between the query representation and each of the candidate bounding boxes. We make use of PHOC embedding as it has previously achieved significant success in segmentation- based word spotting. Word candidates are generated using a simple procedure based on grouping connected components using some spatial constraints. Experiments show that R-PHOC which operates on images directly can improve the current state-of- the-art in the standard GW dataset and performs as good as PHOCNET in some cases designed for segmentation based word spotting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
