In this article, we propose a new approach to segmentation-free word spotting that is based on the combination of three different contributions. Firstly, inspired by the success of bounding box proposal algorithms in object recognition, we propose a scheme to generate a set of word-independent text box proposals. For that, we generate a set of atomic bounding boxes based on simple connected component analysis that are combined using a set of spatial constraints in order to generate the final set of text box proposals. Secondly, an attribute representation based on the Pyramidal Histogram of Characters (PHOC) is encoded in an integral image and used to efficiently evaluate text box proposals for retrieval. Thirdly, we also propose an indexing scheme for fast retrieval based on character n-grams. For the generation of the index a similar attribute space based on a Pyramidal Histogram of Character N-grams (PHON) is used. All attribute models are learned using linear SVMs over the Fisher Vector representation of the word images along with the PHOC or PHON labels of the corresponding words. We show the performance of the proposed approach in both tasks of query-by-string and query-by-example in standard single- and multi-writer data sets, reporting state-of-the-art results.

中文翻译：

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从文档中发现手写单词的文本框建议

在本文中，我们提出了一种基于三种不同贡献的组合的无分段词点识别的新方法。首先，受边界框建议算法在对象识别中成功的启发，我们提出了一种生成一组与单词无关的文本框建议的方案。为此，我们基于简单的连接组件分析生成了一组原子边界框，这些原子边界框使用一组空间约束进行组合以生成最终的文本框建议集。其次，将基于字符金字塔形直方图（PHOC）的属性表示形式编码在完整图像中，并用于有效评估文本框建议以进行检索。第三，我们还提出了一种基于字符n元语法的快速检索索引方案。为了生成索引，使用了基于字符N-gram的金字塔形直方图（PHON）的相似属性空间。使用线性SVM在单词图像的Fisher向量表示形式以及相应单词的PHOC或PHON标签上学习所有属性模型。我们将在标准单作者和多作者数据集中按字符串查询和按示例查询的任务中展示所提出方法的性能，并报告最新的结果。