Reverse engineering for chart data extraction from static images is explored in the literature and softwares, but it is not the focus of this work. Furthermore, deep learning methods have been widely used by various chart recognition works. Classification and detection experiments use deep learning models for each step since these methods present outstanding results in several Computer Vision tasks. When considering each taskвЂ™s results and the process at hand, it is possible to create applications and models that address the current needs of research on this area, such as preparing chart images for data extraction, image tagging for search, and usage on real-time scenarios. The results of our experiment presented accuracy in accordance with the most recent challenges in its respective areas. The experiments were conducted with state-of-the-art techniques, using datasets of chart images collected from the internet and adapted for each task, with evaluations for each method, in order to analyze the efficiency and efficacy of adding these two steps in the chart recognition process. This work presents an evaluation of chart classification, paired with experiments on each task of the chart recognition process, namely, detection of charts on document images, and perspective correction using image rectification. In the context of chart recognition, we propose an approach for classification, detection, and image rectification on static chart images. This figure also highlights the fundamental initial step of chart recognition (blue area of Figure 2), as the focus from here onward. The information visualization pipeline is the key of the main process in the reconstruction of new charts, as it can be directly applied on new environments, being used to overlay a new chart in place of the recognized one or simply store the data. The main process of automatic data chart extraction in Figure 2 covers the steps from the input image to the interaction with the rebuilt chart. After the data extraction, it is simple to rebuild the chart, while using a visualization library or software. This reverse-engineering for chart data extraction from static images is explored in the literature and softwares, with algorithms and methods for many chart types. The process of automatic chart data extraction has two main steps : chart recognition and data extraction following a specific extraction algorithm for each type of data chart. The results showed that, with slight changes, chart recognition methods are now ready for real-world charts, when taking time and accuracy into consideration. 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.

Datathief barplot two references