MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911942826786816 |
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| author | Chen, Lei Yan, Feng Zhong, Yujie Chen, Shaoxiang Jie, Zequn Ma, Lin |
| author_facet | Chen, Lei Yan, Feng Zhong, Yujie Chen, Shaoxiang Jie, Zequn Ma, Lin |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02842 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis Chen, Lei Yan, Feng Zhong, Yujie Chen, Shaoxiang Jie, Zequn Ma, Lin Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language 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/. |
| title | MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2407.02842 |