DocFormerv2: Local Features for Document Understanding

• URL: http://arxiv.org/abs/2306.01733v1
• Date: Fri, 2 Jun 2023 17:58:03 GMT
• Title: DocFormerv2: Local Features for Document Understanding
• Authors: Srikar Appalaraju, Peng Tang, Qi Dong, Nishant Sankaran, Yichu Zhou, R. Manmatha
• Abstract summary: We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU) The VDU domain entails understanding documents (beyond mere OCR predictions) e.g., extracting information from a form. Our approach, termed DocFormerv2 is an encoder-decoder transformer which takes as input - vision, language and spatial features.
• Score: 15.669112678509522
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU). The VDU domain entails understanding documents (beyond mere OCR predictions) e.g., extracting information from a form, VQA for documents and other tasks. VDU is challenging as it needs a model to make sense of multiple modalities (visual, language and spatial) to make a prediction. Our approach, termed DocFormerv2 is an encoder-decoder transformer which takes as input - vision, language and spatial features. DocFormerv2 is pre-trained with unsupervised tasks employed asymmetrically i.e., two novel document tasks on encoder and one on the auto-regressive decoder. The unsupervised tasks have been carefully designed to ensure that the pre-training encourages local-feature alignment between multiple modalities. DocFormerv2 when evaluated on nine datasets shows state-of-the-art performance over strong baselines e.g. TabFact (4.3%), InfoVQA (1.4%), FUNSD (1%). Furthermore, to show generalization capabilities, on three VQA tasks involving scene-text, Doc- Formerv2 outperforms previous comparably-sized models and even does better than much larger models (such as GIT2, PaLi and Flamingo) on some tasks. Extensive ablations show that due to its pre-training, DocFormerv2 understands multiple modalities better than prior-art in VDU.

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