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arXiv · 2212.09621

Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding

Haoli Bai·Zhiguang Liu·Xiaojun Meng·Wentao Li·Shuang Liu·Nian Xie·Rongfu Zheng·Liangwei Wang·Lu Hou·Jiansheng Wei·Xin Jiang·Qun Liu
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Abstract

Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far. A document textline usually contains words that are spatially and semantically correlated, which can be easily obtained from OCR engines. In this paper, we propose Wukong-Reader, trained with new pre-training objectives to leverage the structural knowledge nested in document textlines. We introduce textline-region contrastive learning to achieve fine-grained alignment between the visual regions and texts of document textlines. Furthermore, masked region modeling and textline-grid matching are also designed to enhance the visual and layout representations of textlines. Experiments show that our Wukong-Reader has superior performance on various VDU tasks such as information extraction. The fine-grained alignment over textlines also empowers Wukong-Reader with promising localization ability.

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Haoli Bai, Zhiguang Liu, Xiaojun Meng, Wentao Li, Shuang Liu, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu. 2022-12-19. Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding. https://arxiv.org/abs/2212.09621

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