DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer
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Understanding documents with rich layouts is an essential step towards information extraction. Business intelligence processes often require the extraction of useful semantic content from documents at a large scale for subsequent decision-making tasks. In this context, instance-level segmentation of different document objects(title, sections, figures, tables and so on) has emerged as an interesting problem for the document layout analysis community. To advance the research in this direction, we present a transformer-based model for end-to-end segmentation of complex layouts in document images. To our knowledge, this is the first work on transformer-based document segmentation. Extensive experimentation on the PubLayNet dataset shows that our model achieved comparable or better segmentation performance than the existing state-of-the-art approaches. We hope our simple and flexible framework could serve as a promising baseline for instance-level recognition tasks in document images.
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