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In: International symposium on visual computing. Mishchenko A, Vassilieva N (2011) Model-based chart image classification.

Shukla S Samal A Recognition and quality assessment of data charts in mixed-mode documents Int J Doc Anal Recognit (IJDAR) 2008 11 3 111 10.1007/s1003-5 Google Scholar Digital Library In: Proceedings of the 2007 ACM symposium on Document engineering, pp 9–18 Google Scholar Huang W, Tan CL (2007) A system for understanding imaged infographics and its applications. Khan M Khan SS Data and information visualization methods, and interactive mechanisms: a survey Int J Comput Appl 2011 34 1 1 14 Google Scholar Cross Ref Chen M Golan A What may visualization processes optimize? IEEE Trans Visual Comput Graph 2015 22 12 2619 2632 10.1109/TVCG.2015.2513410 Google Scholar Digital Library Karthikeyani V Nagarajan S Machine learning classification algorithms to recognize chart types in portable document format (pdf) files Int J Comput Appl 2012 39 2 1 5 Google Scholar Cross Ref Extensive comparative evaluations demonstrate that our approach significantly outperforms other benchmark methods, including both traditional and deep learning models, over publicly available datasets. The loss function of the model is improved, which is fused by a variation aware dissimilarity index and incorporated with regularization parameters, making model more prone towards dissimilar regions and similar structure charts. Our approach jointly learns the features of both dissimilar and similar regions using notion of homogeneity. In this paper, we present a novel dissimilarity based learning model for similar structured but diverse chart classification, by improving the loss function. Hence, it becomes difficult for any model to learn the diversity of chart classes with the changing structures due to lack of association between the features from similar-dissimilar regions and its varied structure. These variations differ across all chart types and sub-types. Classification of charts is a major challenge because each chart class has variations due to the styles, appearances, structure, and noises caused due to changing data values.