  We propose a new approach for paragraph recognition in document images by
spatial graph convolutional networks (GCN) applied on OCR text boxes. Two
steps, namely line splitting and line clustering, are performed to extract
paragraphs from the lines in OCR results. Each step uses a beta-skeleton graph
constructed from bounding boxes, where the graph edges provide efficient
support 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 based
models, while achieving comparable or better accuracies on PubLayNet and other
datasets. Furthermore, the GCN models show good generalization from synthetic
training data to real-world images, and good adaptivity for variable document
styles.
