提出分层模型与并行推理，加速表格识别且保持高精度。
Hierarchical Modeling Approach to Fast and Accurate Table Recognition
- 用非因果注意力捕捉全表结构，提升整体理解
- 并行推理使单元格内容识别速度显著提升
- 在两个公开数据集上验证效果，适合文档智能场景
从大量文档中提取和利用多样化知识是智能信息检索中的紧迫挑战。文档包含需不同识别方法的元素。表格识别通常包括三个子任务：表格结构、单元格位置和单元格内容识别。近期模型通过多任务学习、局部注意力和互学习结合，实现了优异的识别效果。然而，其有效性尚未得到充分解释，且推理耗时较长。本文提出一种新型多任务模型，利用非因果注意力捕获整个表格结构，并采用并行推理算法实现更快的单元格内容识别。在两个大型公开数据集上，该方法在视觉和统计上均表现出优越性。
原文摘要 · Abstract (English)
The extraction and use of diverse knowledge from numerous documents is a pressing challenge in intelligent information retrieval. Documents contain elements that require different recognition methods. Table recognition typically consists of three subtasks, namely table structure, cell position and cell content recognition. Recent models have achieved excellent recognition with a combination of multi-task learning, local attention, and mutual learning. However, their effectiveness has not been fully explained, and they require a long period of time for inference. This paper presents a novel multi-task model that utilizes non-causal attention to capture the entire table structure, and a parallel inference algorithm for faster cell content inference. The superiority is demonstrated both visually and statistically on two large public datasets.
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