  Multimodal Large Language Models (MLLM) have made significant progress in the
field of document analysis. Despite this, existing benchmarks typically focus
only on extracting text and simple layout information, neglecting the complex
interactions between elements in structured documents such as mind maps and
flowcharts. To address this issue, we introduce the new benchmark named
MindBench, which not only includes meticulously constructed bilingual authentic
or synthetic images, detailed annotations, evaluation metrics and baseline
models, but also specifically designs five types of structured understanding
and parsing tasks. These tasks include full parsing, partial parsing,
position-related parsing, structured Visual Question Answering (VQA), and
position-related VQA, covering key areas such as text recognition, spatial
awareness, relationship discernment, and structured parsing. Extensive
experimental results demonstrate the substantial potential and significant room
for improvement in current models' ability to handle structured document
information. We anticipate that the launch of MindBench will significantly
advance research and application development in structured document analysis
technology. MindBench is available at:
https://miasanlei.github.io/MindBench.github.io/.
