Evaluation of Convolutional Neural Network Architectures for Chart Image Classification

Chagas, Paulo; Akiyama, Rafael Daisuke; Meiguins, Aruanda Simoes Goncalves; Santos, Carlos Gustavo Resque dos; Saraiva, Filipe; Meiguins, Bianchi Serique; Morais, Jefferson

doi:10.1109/ijcnn.2018.8489315

Cited by 26 publications

(33 citation statements)

References 22 publications

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“…The process of chart recognition can be used in many scenarios, such as indexing, storage of data, and real time overlay of information. While many works [3,5,9] focused on chart classification, only a few addressed the chart detection problem on documents [10,11]. The chart detection in documents can use general approaches of other vision tasks for it context, as we used state-of-the-art models and methods of the MS-COCO challenge, and it can be amplified enough to use techniques of document analysis research field, like the approaches of real-world photography in document images.…”

Section: Discussionmentioning

confidence: 99%

“…A CNN groups the filters into hierarchical layers, learning complex and abstract representations from the dataset [25], and orders the filters as layers. State-of-the-art architectures are being used on recent works for chart recognition [3,5,9,19], focusing on the ones that won the ILSVRC challenge [24]. The main ones, which are present in most deep learning textbooks and courses, are: VGG [26], ResNet [27], MobileNet [28] and Inception [29].…”

Section: Image Classificationmentioning

confidence: 99%

“…Chagas et al [9] proposed an evaluation of more robust CNN models for chart image classification. Unlike the previously cited works, the proposed methodology has two main tasks: training using synthetically generated images only, comparing the CNN models with conventional classifiers, such as decision trees and support vector machines.…”

Section: Related Workmentioning

confidence: 99%

“…In this way, chart recognition can be defined as a process that is composed of three computer vision tasks: classification, detection, and perspective correction. Following the literature, the main usage scenario of chart recognition is to discover the chart type [2,3,5,[8][9][10][11], using it to choose a proper data extraction pipeline. Nevertheless, even without data extraction, there are scenarios that chart recognition can be applied to.…”

Section: Introductionmentioning

confidence: 99%

“…Classification and detection experiments use deep learning models for each step since these methods present outstanding results in several Computer Vision tasks. Furthermore, deep learning methods have been widely used by various chart recognition works [3,5,9,18,19]. 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.…”

Section: Introductionmentioning

confidence: 99%

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A Real-World Approach on the Problem of Chart Recognition Using Classification, Detection and Perspective Correction

Araújo

Chagas

Alves

et al. 2020

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%