Dynamic Relation Transformer for Contextual Text Block Detection
Jiawei Wang Shunchi Zhang Kai Hu Chixiang Ma Zhuoyao Zhong Lei Sun Qiang Huo
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.