LDRNet: Enabling Real-time Document Localization on Mobile Devices

TL;DR

LDRNet is a lightweight, real-time document localization model for mobile devices that significantly improves speed while maintaining accuracy, enabling more efficient ID verification processes without human intervention.

Contribution

The paper introduces LDRNet, a novel lightweight model with unique prediction branches and loss functions, optimized for real-time document localization on mobile devices.

Findings

LDRNet achieves up to 790 FPS, 47 times faster than existing methods.

LDRNet maintains comparable accuracy with other approaches in document localization.

The model is suitable for various mobile applications requiring fast document detection.

Abstract

While Identity Document Verification (IDV) technology on mobile devices becomes ubiquitous in modern business operations, the risk of identity theft and fraud is increasing. The identity document holder is normally required to participate in an online video interview to circumvent impostors. However, the current IDV process depends on an additional human workforce to support online step-by-step guidance which is inefficient and expensive. The performance of existing AI-based approaches cannot meet the real-time and lightweight demands of mobile devices. In this paper, we address those challenges by designing an edge intelligence-assisted approach for real-time IDV. Aiming at improving the responsiveness of the IDV process, we propose a new document localization model for mobile devices, LDRNet, to Localize the identity Document in Real-time. On the basis of a lightweight backbone network, we build three prediction branches for LDRNet, the corner points prediction, the line borders prediction and the document classification. We design novel supplementary targets, the equal-division points, and use a new loss function named Line Loss, to improve the speed and accuracy of our approach. In addition to the IDV process, LDRNet is an efficient and reliable document localization alternative for all kinds of mobile applications. As a matter of proof, we compare the performance of LDRNet with other popular approaches on localizing general document datasets. The experimental results show that LDRNet runs at a speed up to 790 FPS which is 47x faster, while still achieving comparable Jaccard Index(JI) in single-model and single-scale tests.