Challenges in chart image classification: a comparative study of different deep learning methods

Abstract

Charts are commonly used forms of visualizing scientific observations from research findings or commercial trends. They provide an abstraction of the underlying information in a more understandable way. Over time, different forms of charts are developed. With the increase in the number of scientific documents present on the internet with different types of charts, automatic chart classification is becoming an important task for various applications. There have been several studies on chart classification with methods ranging from traditional machine learning approaches like SVM, KNN, and HMM to recent deep learning models like VGG, ResNet, and Xception. However, inconsistencies in experimental results are evident. This paper evaluates nine of the recently proposed deep learning-based models on three datasets (one curated and annotated by authors, and two publicly available), and systematically studies their performances over various setups to understand the reason for observing inconsistent results.