  Flowcharts are graphical tools for representing complex concepts in concise
visual representations. This paper introduces the FlowLearn dataset, a resource
tailored to enhance the understanding of flowcharts. FlowLearn contains complex
scientific flowcharts and simulated flowcharts. The scientific subset contains
3,858 flowcharts sourced from scientific literature and the simulated subset
contains 10,000 flowcharts created using a customizable script. The dataset is
enriched with annotations for visual components, OCR, Mermaid code
representation, and VQA question-answer pairs. Despite the proven capabilities
of Large Vision-Language Models (LVLMs) in various visual understanding tasks,
their effectiveness in decoding flowcharts - a crucial element of scientific
communication - has yet to be thoroughly investigated. The FlowLearn test set
is crafted to assess the performance of LVLMs in flowchart comprehension. Our
study thoroughly evaluates state-of-the-art LVLMs, identifying existing
limitations and establishing a foundation for future enhancements in this
relatively underexplored domain. For instance, in tasks involving simulated
flowcharts, GPT-4V achieved the highest accuracy (58%) in counting the number
of nodes, while Claude recorded the highest accuracy (83%) in OCR tasks.
Notably, no single model excels in all tasks within the FlowLearn framework,
highlighting significant opportunities for further development.
