# LDRNet: Enabling Real-time Document Localization on Mobile Devices

arXiv: https://arxiv.org/abs/2206.02136 · HTML: https://mlsanity.com/paper/2206.02136/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 5 Jun 2022.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Temporal Segment Networks for Action Recognition in Videos](https://mlsanity.com/paper/1705.02953/) (2017)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Towards Accurate Multi-person Pose Estimation in the Wild](https://mlsanity.com/paper/1701.01779/) (2017)
- [Hypercolumns for Object Segmentation and Fine-grained Localization](https://mlsanity.com/paper/1411.5752/) (2014)
- [Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation](https://mlsanity.com/paper/1705.00389/) (2017)
- [Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation](https://mlsanity.com/paper/1903.02120/) (2019)
- [Toward fast and accurate human pose estimation via soft-gated skip connections](https://mlsanity.com/paper/2002.11098/) (2020)
- [MIDV-2019: Challenges of the modern mobile-based document OCR](https://mlsanity.com/paper/1910.04009/) (2019)

## Built on by (most cited first, 2 of 2)

- [DocXPand-25k: a large and diverse benchmark dataset for identity documents analysis](https://mlsanity.com/paper/2407.20662/) (2024)
- [Data Efficient Training of a U-Net Based Architecture for Structured Documents Localization](https://mlsanity.com/paper/2310.00937/) (2023)

## Abstract (arXiv)

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.