Before revision:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts and multimodal documents such as name cards. We show that SPADE\spadesuit outperforms the previous BIO tagging-based approach on name card parsing task and achieves comparable performance on receipt parsing task. Especially, when the receipt images have non-flat manifold representing physical distortion of receipt paper in real-world, SPADE\spadesuit outperforms the tagging-based method by a large margin of 25.8\% highlighting the strong performance of SPADE\spadesuit over spatially complex document .
After revision:  To tackle these issues, we propose SPADE\spadesuit (SPatial DEpendency parser) , an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers , making it suitable for parsing structure-rich documents such as receipts , name cards, forms, and invoices, and show that it achieves a similar or better performance compared to strong baselines including BERT-based IOB taggger, with up to 37.7\% improvement .