Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends
Mirna Al-Shetairy, Hanan Hindy, Dina Khattab, Mostafa M. Aref

TL;DR
This review discusses recent transformer-based advances in chart understanding, highlighting new architectures, datasets, and challenges, and outlining future research directions in multimodal vision-language tasks involving charts.
Contribution
It provides a comprehensive analysis of transformer-based frameworks for chart understanding, categorizes tasks, and identifies key challenges and future trends in the field.
Findings
Transformers significantly improved chart understanding performance.
Multi-task frameworks and pre-training techniques are prominent.
Challenges include OCR dependency and low-resolution image handling.
Abstract
In recent years, interest in vision-language tasks has grown, especially those involving chart interactions. These tasks are inherently multimodal, requiring models to process chart images, accompanying text, underlying data tables, and often user queries. Traditionally, Chart Understanding (CU) relied on heuristics and rule-based systems. However, recent advancements that have integrated transformer architectures significantly improved performance. This paper reviews prominent research in CU, focusing on State-of-The-Art (SoTA) frameworks that employ transformers within End-to-End (E2E) solutions. Relevant benchmarking datasets and evaluation techniques are analyzed. Additionally, this article identifies key challenges and outlines promising future directions for advancing CU solutions. Following the PRISMA guidelines, a comprehensive literature search is conducted across Google…
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Taxonomy
TopicsFault Detection and Control Systems · Advanced Statistical Process Monitoring · Engineering and Test Systems
