Page Object Detection from PDF Document Images by Deep Structured Prediction and Supervised Clustering

Xiaohui Li, Fei Yin, Cheng-Lin Liu

Abstract: Page object detection in document images remains a challenge because the page objects are diverse in scale and aspect ratio, and an object may contain largely apart components. In this paper, we propose a hybrid method combining deep structured prediction and supervised clustering to detect formulas, tables and figures in PDF document images within a unified framework. The primitive region proposals extracted from each column region are classified and clustered with conditional random field (CRF) based graphical models which can integrate both local and contextual information. Both the unary and pairwise potentials of CRFs are formulated as convolutional neural networks (CNNs) to better exploit spatial contextual information. The CRF for clustering predicts the linked/cut label of between-region links. After CRF inference, the line regions of same class within a cluster are grouped into a page object. The state-of-the-art performance obtained on the public available ICDAR2017 POD competition dataset demonstrates the effectiveness and superiority of the nronosed method.