Abstract
Contextual Text Block Detection (CTBD) is the task of identifying coherent text blocks within the complexity of natural scenes. Previous methodologies have treated CTBD as either a visual relation extraction challenge within computer vision or as a sequence modeling problem from the perspective of natural language processing. We introduce a new framework that frames CTBD as a graph generation problem. This methodology consists of two essential procedures: identifying individual text units as graph nodes and discerning the sequential reading order relationships among these units as graph edges. Leveraging the cutting-edge capabilities of DQ-DETR for node detection, our framework innovates further by integrating a novel mechanism, a Dynamic Relation Transformer (DRFormer), dedicated to edge generation. DRFormer incorporates a dual interactive transformer decoder that deftly manages a dynamic graph structure refinement process. Through this iterative process, the model systematically enhances the graph's fidelity, ultimately resulting in improved precision in detecting contextual text blocks. Comprehensive experimental evaluations conducted on both SCUT-CTW-Context and ReCTS-Context datasets substantiate that our method achieves state-of-the-art results, underscoring the effectiveness and potential of our graph generation framework in advancing the field of CTBD.
Abstract (translated)
上下文文本块检测（CTBD）是在自然场景的复杂性中识别连贯文本块的任务。之前的方法将CTBD视为计算机视觉中的关系提取挑战或自然语言处理角度下的序列建模问题。我们引入了一种新的框架，将CTBD视为图生成问题。该方法包括两个基本步骤：将单个文本单元识别为图节点，并分辨这些单元之间的序列阅读顺序关系作为图边。利用DQ-DETR节点检测的先进功能，我们的框架通过引入一种名为动态关系Transformer（DRFormer）的新机制进一步创新，该机制专门用于边生成。DRFormer包括一个双交互式变压器解码器，巧妙地管理了动态图结构的细化过程。通过这一迭代过程，模型系统地增强了图的可靠性，最终在检测上下文文本块方面实现了更高的准确度。在SCUT-CTW-Context和ReCTS-Context数据集上进行的全局实验评估证实了我们的方法达到了最先进水平，进一步突出了我们图生成框架在推进CTBD领域中的有效性和潜力。
URL
https://arxiv.org/abs/2401.09232
