Abstract
In recovering information from the chart image, the first step should be chart type classification. Throughout history, many approaches have been used, and some of them achieve results better than others. The latest articles are using a Support Vector Machine (SVM) in combination with a Convolutional Neural Network (CNN), which achieve almost perfect results with the datasets of few thousand images per class. The datasets containing chart images are primarily synthetic and lack real-world examples. To overcome the problem of small datasets, to our knowledge, this is the first report of using Siamese CNN architecture for chart type classification. Multiple network architectures are tested, and the results of different dataset sizes are compared. The network verification is conducted using Few-shot learning (FSL). Many of described advantages of Siamese CNNs are shown in examples. In the end, we show that the Siamese CNN can work with one image per class, and a 100% average classification accuracy is achieved with 50 images per class, where the CNN achieves only average classification accuracy of 43% for the same dataset.
Highlights
Which model achieves the highest classification on chart which model achieves the the highest average classification accuracy on chart images
This paper focuses on the classification of chart images using the Siamese Convolutional Neural Network (CNN), which has never been conducted before
The conducted research proves that Siamese CNN can be used with chart type classification
Summary
The majority of the used data visualizations are “locked” inside the documents, which can be digitized These documents contain graphical and textual information linked together in one visual unit. The first challenge in retrieving information from digitized data visualization images is classifying that image in one of many existing chart classes. CNN can achieve state-of-the-art results in many computer vision tasks This method requires many images (often 1000 or more per class) to be successful. To deal with the presented problem, and to our knowledge, we are the first to introduce the Siamese CNN in chart type classification.
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