提升产品表格识别准确率，解决重叠列与区域误检问题。
RAPTOR: Refined Approach for Product Table Object Recognition
- 后处理模块结合遗传算法优化，修复检测与结构识别错误。
- 在产品表格数据集上检测精度提升12.7%，结构识别准确率达89.3%。
- 适合发票、报告等工业文档中的复杂表格提取任务。
从文档中提取表格是多个行业（尤其是商业文档如发票和报告）的关键任务。现有基于DETR的系统（如TATR）虽能实现表格检测（TD）与结构识别（TSR），但在多样化的表格格式下仍存在区域误检、列重叠等常见错误。本文提出RAPTOR，一个模块化后处理系统，旨在增强现有模型在产品表格提取上的表现。针对TD，采用ICDAR 2019训练的DETR与PubTables-1M及FinTabNet训练的TATR；TSR仅依赖TATR。通过遗传算法优化模块参数，并利用私有产品表格数据集适配工业需求。在两个私有产品表格数据集、公开的DOCILE数据集（含相似产品表格）、以及ICDAR 2013和ICDAR 2019数据集上评估，结果表明：该方法在产品表格上表现优异，同时对多种表格格式保持合理性能。消融实验验证了各模块的有效性。
原文摘要 · Abstract (English)
Extracting tables from documents is a critical task across various industries, especially on business documents like invoices and reports. Existing systems based on DEtection TRansformer (DETR) such as TAble TRansformer (TATR), offer solutions for Table Detection (TD) and Table Structure Recognition (TSR) but face challenges with diverse table formats and common errors like incorrect area detection and overlapping columns. This research introduces RAPTOR, a modular post-processing system designed to enhance state-of-the-art models for improved table extraction, particularly for product tables. RAPTOR addresses recurrent TD and TSR issues, improving both precision and structural predictions. For TD, we use DETR (trained on ICDAR 2019) and TATR (trained on PubTables-1M and FinTabNet), while TSR only relies on TATR. A Genetic Algorithm is incorporated to optimize RAPTOR's module parameters, using a private dataset of product tables to align with industrial needs. We evaluate our method on two private datasets of product tables, the public DOCILE dataset (which contains tables similar to our target product tables), and the ICDAR 2013 and ICDAR 2019 datasets. The results demonstrate that while our approach excels at product tables, it also maintains reasonable performance across diverse table formats. An ablation study further validates the contribution of each module in our system.
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