Text Role Classification in Scientific Charts Using Multimodal Transformers

Text role classification involves classifying the semantic role of textual elements within scientific charts. We propose to finetune the multimodal document layout analysis models LayoutLMv3 and UDOP for this task. The transformers utilize the three modalities of text, image, and layout as input. We further investigate how data augmentation and balancing methods affect performance. The models are evaluated on various chart datasets, and results show that LayoutLMv3 outperforms UDOP in all experiments. LayoutLMv3 achieves the highest F1-macro score of 82.87 on the ICPR22 test dataset, beating the best-performing model from the ICPR22 CHART-Infographics challenge. Moreover, the robustness of the models is tested on a synthetic noisy dataset ICPR22-N. Finally, the generalizability of the models is evaluated on three chart datasets, CHIME-R, DeGruyter, and EconBiz, for which we added labels for the text roles. Findings indicate that even in cases where there is limited training data, transformers can be used with the help of data augmentation and balancing methods. The source code and datasets are available on GitHub: https://github.com/hjkimk/text-role-classification .