LED: A Benchmark for Evaluating Layout Error Detection in Document Analysis
Recent advances in Large Language Models (LLMs) and Large Multimodal Models (LMMs) have improved Document Layout Analysis (DLA), yet structural errors such as region merging, splitting, and omission remain persistent. Conventional overlap-based metrics (e.g., IoU, mAP) fail to capture such logical inconsistencies. To overcome this limitation, we propose Layout Error Detection (LED), a benchmark that evaluates structural reasoning in DLA predictions beyond surface-level accuracy. LED defines eight standardized error types (Missing, Hallucination, Size Error, Split, Merge, Overlap, Duplicate, and Misclassification) and provides quantitative rules and injection algorithms for realistic error...
| Authors | Inbum Heo, Taewook Hwang, Jeesu Jung et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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