Structured documents analysis and recognition are essential for modern online
on-boarding processes, and document localization is a crucial step to achieve
reliable key information extraction. While deep-learning has become the
standard technique used to solve document analysis problems, real-world
applications in industry still face the limited availability of labelled data
and of computational resources when training or fine-tuning deep-learning
models. To tackle these challenges, we propose SDL-Net: a novel U-Net like
encoder-decoder architecture for the localization of structured documents. Our
approach allows pre-training the encoder of SDL-Net on a generic dataset
containing samples of various document classes, and enables fast and
data-efficient fine-tuning of decoders to support the localization of new
document classes. We conduct extensive experiments on a proprietary dataset of
structured document images to demonstrate the effectiveness and the
generalization capabilities of the proposed approach.

结构化文档分析和识别对于现代在线入职流程至关重要，文档定位是实现可靠关键信息提取的关键步骤。本研究提出了 SDL-Net：一种新的 U-Net 类编码器 - 解码器架构，用于定位结构化文档。我们的方法可以在包含多种文档类别样本的通用数据集上预训练 SDL-Net 的编码器，并支持对新的文档类别进行快速和数据高效的解码器微调，我们在一系列专有结构化文档图像数据集上进行了大量实验证明了所提方法的有效性和广泛适用性。

基于 U-Net 的结构化文档定位的数据高效训练

Data Efficient Training of a U-Net Based Architecture for Structured  Documents Localization

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.

本文提出了基于边缘智能辅助的方法，设计了一个实时身份证验证（IDV）系统，LDRNet，用于在移动设备上本地化身份文件，增强 IDV 过程的响应性和准确率。