This review paper offers a contemporary literature survey on symbol spotting in architectural drawing images. Research on isolated symbol recognition is quite mature; the same cannot be said for recognizing a symbol in context. One important challenge is the segmentation/recognition paradox: a system should segment symbols before recognizing them, but some kind of recognition may be necessary to obtain a correct segmentation. Research has thus been recently directed toward symbol spotting, a way of locating possible symbol instances without using full recognition methods. In this paper, we thoroughly review symbol spotting methods with a focus on architectural drawings, an application domain providing the document image analysis and graphic recognition communities with an interesting set of challenges linked to the sheer complexity and density of embedded information, that have yet to be resolved. While most existing methods perform well in terms of recall, their performance is rather poor in terms of precision and false positives. In light of the review, we also propose a simple yet effective symbol spotting method based on template matching and a novel clutter-tolerant cross-correlation function that achieves state-of-the-art results with high precision, high recall, and few false positives, able to cope with “real-life clutter” found in industry-standard architectural drawings.

中文翻译：

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建筑图纸的符号识别：最新技术和行业驱动的新发展

这篇评论文章提供了有关建筑绘图图像中的符号斑点的当代文献调查。孤立符号识别的研究已经相当成熟。在上下文中识别符号不能说相同的话。一个重要的挑战是分段/识别悖论：系统应该在识别符号之前先对符号进行分段，但是为了获得正确的分段可能需要某种识别。因此，最近的研究方向是符号定位，这是一种无需使用完全识别方法即可定位可能的符号实例的方式。在本文中，我们以建筑图纸为重点，全面回顾了符号识别方法，一个应用程序域，为文档图像分析和图形识别社区提供了一系列有趣的挑战，这些挑战与嵌入式信息的复杂性和密度有关，尚待解决。尽管大多数现有方法在召回方面表现良好，但在准确性和误报方面却表现不佳。有鉴于此，我们还提出了一种基于模板匹配的简单而有效的符号识别方法，以及一种新颖的，具有杂波容忍的互相关函数，该函数以高精度，高召回率和极少的错误实现了最新的结果积极的态度，能够应付行业标准建筑图纸中出现的“现实混乱”。就精确度和误报而言，它们的性能相当差。有鉴于此，我们还提出了一种基于模板匹配的简单而有效的符号识别方法，以及一种新颖的，具有杂波容忍的互相关函数，该函数以高精度，高召回率和极少的错误实现了最新的结果积极的态度，能够应付行业标准建筑图纸中出现的“现实混乱”。就精确度和误报而言，它们的性能相当差。有鉴于此，我们还提出了一种基于模板匹配的简单而有效的符号识别方法，以及一种新颖的，具有杂波容忍的互相关函数，该函数以高精度，高召回率和极少的错误实现了最新的结果积极的态度，能够应付行业标准建筑图纸中出现的“现实混乱”。