Modern Chart Recognition methods do not cover real-world situations like this one. The chart is in the middle of a book page and slightly tilted, which indicates that it needs some perspective correction.Ī Bar chart on real-world photography: tilted and in the middle of the text. Figure 1 illustrates an example of this situation. This factor means that the chart can be misaligned and might need some correction for the extraction step. Additionally, in a real-world photograph, there is the factor of perspective to take into account. This detection aims to locate and extract the data chart only, improving recognition performance.

When the input image contains other elements besides the chart image (text labels, for example), the detection of these charts must come as a prior step. Like so, automatic methods could be applied to perform the chart analysis from these images, aiming to obtain the raw data.

However, in the majority of cases, these charts are displayed as static images, which means that the original data are not usually available. With the original chart data available, it is possible to perform the necessary changes for mitigating the problems that are presented earlier. A redesign of those visual representations might be needed to fix these misconceptions. For example, wrong chart choice and poor mapping of visual variables can reduce the chart quality due to lack of relevant items, such as labels, names of axes, or subtitles. In the same way, wrong design choices on chart generation can lead to misinterpretation or later preclude correct data analysis. In general, a well-designed data chart leads to an intuitive understanding of its underlying data. The results showed that, with slight changes, chart recognition methods are now ready for real-world charts, when taking time and accuracy into consideration.ĭata charts are widely used in technical, scientific, and financial documents, being present in many other subjects of our daily lives, such as newspapers, magazines, web pages, and books. 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. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. Other features in real-world images that can make this task difficult are photo distortions, noise, alignment, etc. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. In the same way, when data charts have wrong design choices, a redesign of these representations might be needed. In general, a well-constructed data chart leads to an intuitive understanding of its underlying data. Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others.

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