Computer Science Graduate Program (PPGCC), Federal University of Pará (UFPA), 66075-110 Belém, Brazil.
Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Department of Electronics, Telecommunications e Informatics (DETI), University of Aveiro (UA), 3810-193 Aveiro, Portugal.
Sensors (Basel). 2020 Aug 5;20(16):4370. doi: 10.3390/s20164370.
Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. In general, a well-constructed data chart leads to an intuitive understanding of its underlying data. In the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Other features in real-world images that can make this task difficult are photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. This paper proposes a classification, detection, and perspective correction process that is suitable for real-world usage, when considering the data used for training a state-of-the-art model for the extraction of a chart in real-world photography. The results showed that, with slight changes, chart recognition methods are now ready for real-world charts, when taking time and accuracy into consideration.
数据图表在我们的日常生活中被广泛使用，存在于各种媒体中，如报纸、杂志、网页、书籍等。通常，一个构造良好的数据图表可以直观地理解其底层数据。同样，当数据图表有错误的设计选择时，可能需要重新设计这些表示。然而，在大多数情况下，这些图表是以静态图像的形式呈现的，这意味着原始数据通常不可用。因此，可以应用自动方法从图表图像中提取底层数据，以允许进行这些更改。识别图表并从中提取数据的任务非常复杂，主要是因为图表类型的多样性及其视觉特征。现实世界图像中的其他特征也会使这项任务变得困难，例如照片失真、噪声、对齐等。有两种计算机视觉技术可以辅助这项任务，而在这种情况下很少被探索，即透视检测和校正。这些方法可以将扭曲和嘈杂的图表转换为清晰的图表，并准备好提取数据或其他用途。本文提出了一种分类、检测和透视校正的过程，适用于真实世界的使用情况，同时考虑到用于在真实世界摄影中提取图表的最先进模型的数据。结果表明，考虑到时间和准确性，图表识别方法现在已经可以适用于真实世界的图表了。
