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

He Guo, Xiameng Qin, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding

Abstract: Extracting Text of Interest (ToI) from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ complicated engineering pipeline, which contains OCR and structure information extraction, to fulfill this task. This paper proposes an Entity-aware Attention Text Extraction Network called EATEN, which is an end-to-end trainable system to extract the ToIs without any post-processing. In the proposed framework, each entity is parsed by its corresponding entity-aware decoder, respectively. Moreover, we innovatively introduce a state transition mechanism which further improves the robustness of visual ToI extraction. In consideration of the absence of public benchmarks, we construct a dataset of almost 0.6 million images in three real-world scenarios (train ticket, passport and business card), which is publicly available at https://github.com/beacandler/EATEN. To the best of our knowledge, EATEN is the first single shot method to extract entities from images. Extensive experiments on these benchmarks demonstrate the state-of-the-art performance of EATEN.