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Extracting entity from images is a crucial part of many OCR applications,such as entity recognition of cards, invoices, and receipts. Most of theexisting works employ classical detection and recognition paradigm. This paperproposes an Entity-aware Attention Text Extraction Network called EATEN, whichis an end-to-end trainable system to extract the entities without anypost-processing. In the proposed framework, each entity is parsed by itscorresponding entity-aware decoder, respectively. Moreover, we innovativelyintroduce a state transition mechanism which further improves the robustness ofentity extraction. In consideration of the absence of public benchmarks, weconstruct a dataset of almost 0.6 million images in three real-world scenarios(train ticket, passport and business card), which is publicly available athttps://github.com/beacandler/EATEN. To the best of our knowledge, EATEN is thefirst single shot method to extract entities from images. Extensive experimentson these benchmarks demonstrate the state-of-the-art performance of EATEN.

EATEN: Entity-aware Attention for Single Shot Visual Text Extraction