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While Identity Document Verification (IDV) technology on mobile devicesbecomes ubiquitous in modern business operations, the risk of identity theftand fraud is increasing. The identity document holder is normally required toparticipate in an online video interview to circumvent impostors. However, thecurrent IDV process depends on an additional human workforce to support onlinestep-by-step guidance which is inefficient and expensive. The performance ofexisting AI-based approaches cannot meet the real-time and lightweight demandsof mobile devices. In this paper, we address those challenges by designing anedge intelligence-assisted approach for real-time IDV. Aiming at improving theresponsiveness of the IDV process, we propose a new document localization modelfor mobile devices, LDRNet, to Localize the identity Document in Real-time. Onthe basis of a lightweight backbone network, we build three prediction branchesfor LDRNet, the corner points prediction, the line borders prediction and thedocument classification. We design novel supplementary targets, theequal-division points, and use a new loss function named Line Loss, to improvethe speed and accuracy of our approach. In addition to the IDV process, LDRNetis an efficient and reliable document localization alternative for all kinds ofmobile applications. As a matter of proof, we compare the performance of LDRNetwith other popular approaches on localizing general document datasets. Theexperimental results show that LDRNet runs at a speed up to 790 FPS which is47x faster, while still achieving comparable Jaccard Index(JI) in single-modeland single-scale tests.

LDRNet: Enabling Real-time Document Localization on Mobile Devices