DocFormer: End-to-End Transformer for Document Understanding

• URL: http://arxiv.org/abs/2106.11539v1
• Date: Tue, 22 Jun 2021 04:28:07 GMT
• Title: DocFormer: End-to-End Transformer for Document Understanding
• Authors: Srikar Appalaraju and Bhavan Jasani and Bhargava Urala Kota and Yusheng Xie and R. Manmatha
• Abstract summary: We present DocFormer -- a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU) VDU is a challenging problem which aims to understand documents in their varied formats (forms, receipts etc.)
• Score: 6.412887519128816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present DocFormer -- a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). VDU is a challenging problem which aims to understand documents in their varied formats (forms, receipts etc.) and layouts. In addition, DocFormer is pre-trained in an unsupervised fashion using carefully designed tasks which encourage multi-modal interaction. DocFormer uses text, vision and spatial features and combines them using a novel multi-modal self-attention layer. DocFormer also shares learned spatial embeddings across modalities which makes it easy for the model to correlate text to visual tokens and vice versa. DocFormer is evaluated on 4 different datasets each with strong baselines. DocFormer achieves state-of-the-art results on all of them, sometimes beating models 4x its size (in no. of parameters).

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