Chart classification: a survey and benchmarking of different state-of-the-art methods

With the increase in the number of documents with various types of charts available on the internet, automatic chart classification has become an essential task for various downstream applications such as chart data recovery, chart replenishment. This paper presents a comprehensive survey of the studies reported in the literature since 2001 from the perspective of the corpus, pre-processing techniques, feature extraction, and methodologies. Considering that the majority of the existing studies use small datasets with a smaller number of chart types and also reported varying performances, this paper implements and evaluates 44 different machine learning-based chart classification models. The evaluation is done over a large dataset curated locally and benchmarks the performances of these 44 different models over a common experimental framework. It also performs a comprehensive error analysis, identifying two core challenging issues (noise in the charts and confusing chart pairs) that affect the chart classification performances. Compared with the existing survey papers, this paper presents a more comprehensive review and experimental analysis.