构建首个面向建筑行业的表格结构识别数据集，助力工程文档智能化处理。
CISOL: An Open and Extensible Dataset for Table Structure Recognition in the Construction Industry
- 聚焦建筑行业真实工程文档，构建可复现、可扩展的标注数据集。
- 含超12万标注实例，800+文档图像，中等规模支持表格识别任务训练。
- 实测性能优于专用模型，适合工业场景表格分析研究者使用。
可复现性是机器学习研究的核心，依赖于模型与训练评估数据集的双重开放。本文提出建筑行业钢料订单列表（CISOL）数据集，强调透明性以保障可复现性与可扩展性。CISOL包含超过800张真实工程文档图像，共12万余个标注实例，是建筑领域首个专注表格结构识别的中等规模数据集。基准测试显示，基于YOLOv8模型在CISOL上达到67.22 [email protected]:0.95:0.05，优于专用的TATR模型，验证其作为专业领域表格识别基准的有效性。
原文摘要 · Abstract (English)
Reproducibility and replicability are critical pillars of empirical research, particularly in machine learning, where they depend not only on the availability of models, but also on the datasets used to train and evaluate those models. In this paper, we introduce the Construction Industry Steel Ordering List (CISOL) dataset, which was developed with a focus on transparency to ensure reproducibility, replicability, and extensibility. CISOL provides a valuable new research resource and highlights the importance of having diverse datasets, even in niche application domains such as table extraction in civil engineering. CISOL is unique in that it contains real-world civil engineering documents from industry, making it a distinctive contribution to the field. The dataset contains more than 120,000 annotated instances in over 800 document images, positioning it as a medium-sized dataset that provides a robust foundation for Table Structure Recognition (TSR) and Table Detection (TD) tasks. Benchmarking results show that CISOL achieves 67.22 [email protected]:0.95:0.05 using the YOLOv8 model, outperforming the TSR-specific TATR model. This highlights the effectiveness of CISOL as a benchmark for advancing TSR, especially in specialized domains.
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