Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods.

中文翻译：

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在不正确的分割场景下评估单词发现

单词发现是大规模检索文档集合中的重要识别任务。在大多数情况下，假设完美的分词方法就可以开发和评估方法。在本文中，我们提出了一个实验框架，用于量化在相同的无偏条件下分词对通过分词方法实现的性能的好处。该框架包括在分割时产生系统的失真，以及从失真的数据集中检索原始查询。我们已经使用George Washington和Barcelona Marriage数据集在几种已建立的最新方法上测试了我们的框架。完成的实验可以估计单词发现方法的端到端性能。