SRRV: A Novel Document Object Detector Based on Spatial-Related Relation and Vision

Document object detection is a challenging task due to layout complexity and object diversity. Most of existing methods mainly focus on vision information, neglecting representative inherent spatial-related relationship among document objects. To capture structural information and contextual dependencies, we propose a novel document object detector based on spatial-related relation and vision (SRRV). It consists of three parts: vision feature extraction network, relation feature aggregation network and result refinement network. Vision feature extraction network enhances information propagation of hierarchical feature pyramid by adopting feature augmentation paths. Then, relation feature aggregation network combines graph construction module and graph learning module. Specifically, graph construction module calculates spatial information from geometric attributes of region proposals to encode relation information, while graph learning module stacks Graph Convolutional Network (GCN) layers to aggregate relation information at global scale. Both the vision and relation features are fed into result refinement network for feature fusion and relational reasoning. Experiments on the PubLayNet, POD and Article Regions datasets demonstrate that spatial relation information improves the performance with better accuracy and more precise bounding box prediction.