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We propose a new approach for paragraph recognition in document images byspatial graph convolutional networks (GCN) applied on OCR text boxes. Twosteps, namely line splitting and line clustering, are performed to extractparagraphs from the lines in OCR results. Each step uses a beta-skeleton graphconstructed from bounding boxes, where the graph edges provide efficientsupport for graph convolution operations. With only pure layout input features,the GCN model size is 3~4 orders of magnitude smaller compared to R-CNN basedmodels, while achieving comparable or better accuracies on PubLayNet and otherdatasets. Furthermore, the GCN models show good generalization from synthetictraining data to real-world images, and good adaptivity for variable documentstyles.

Post-OCR Paragraph Recognition by Graph Convolutional Networks