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ICDAR2017 Competition on Recognition of Documents with Complex Layouts – RDCL2017

机译:ICDAR2017复杂布局文档识别竞赛– RDCL2017

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摘要

This paper presents an objective comparative evaluation of page segmentation and region classification methods for documents with complex layouts. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2017, presenting the results of the evaluation of seven methods – five submitted, two state-of-the-art systems (commercial and open-source). Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmentation and region classification (one focusing only on text regions). For the first time, nested region content (table cells, chart labels etc.) are evaluated in addition to the top-level page content. Text recognition was a bonus challenge and was not taken up by all participants. The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the non-textual content.
机译:本文提出了对具有复杂布局的文档的页面分割和区域分类方法的客观比较评估。它描述了在ICDAR2017范围内举行的竞赛(方法操作,数据集和评估方法),并介绍了七种方法(五种提交的,两种最新系统(商业和开源))的评估结果。本文报告了三种情况,一种评估了方法对区域进行精确分割的能力,另外两种评估了分割和区域分类(一种仅针对文本区域)。除顶级页面内容外,首次评估嵌套区域内容(表格单元格,图表标签等)。文本识别是一项挑战,并非所有参与者都接受。结果表明,创新的方法具有明显的优势,但仍然非常需要开发强大的方法来应对布局挑战,尤其是非文本内容。

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