An End-to-End OCR-Free Solution For Identity Document Information Extraction

Salvatore Carta , Alessandro Giuliani , Leonardo Piano , Sandro Gabriele Tiddia
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Abstract

Efficiently verifying the customer’s identity is essential for ensuring the security of online transactions. Such verification is mainly accomplished according to the widespread Know Your Client (KYC) protocol, which relies on a self-identification handled by the user, typically providing personal data from their identification document (ID). In the current digital communication generation, such a task is usually performed by uploading a digital copy of the document by scanning or taking a real-time picture from a personal device such as a smartphone or tablet. Such activity, which usually involves manual data entry, is time-consuming and prone to human errors. Document Understanding (DU) has emerged as a crucial factor for automating data extraction in this scenario. One of its main challenges is the scarcity of data, which is barely available for security and privacy concerns. To this end, this paper proposes a solution that takes advantage of recent advances in DU to devise an innovative strategy for Identity Document Recognition (IDR), i.e., the task aimed at automatically understanding, extracting, and transcribing the fields of an ID card. We devised a two-stage approach based on fine-tuning a pre-trained model on synthetic and real-world data. We also developed a dedicated synthetic data generation tool to support the IDR process. Experimental results demonstrated the effectiveness of our methodology.
身份证件信息提取的端到端无ocr解决方案
有效地验证客户的身份对于确保网上交易的安全至关重要。这种验证主要是根据广泛的“了解您的客户”(KYC)协议完成的,该协议依赖于用户处理的自我识别,通常提供其身份证件(ID)中的个人数据。在当前的数字通信时代,这样的任务通常是通过扫描或从智能手机或平板电脑等个人设备拍摄实时照片上传文件的数字副本来完成的。此类活动通常涉及手动输入数据,既耗时又容易出现人为错误。文档理解(Document Understanding, DU)已经成为这个场景中自动化数据提取的关键因素。它的主要挑战之一是数据的稀缺性,这些数据几乎无法用于安全和隐私问题。为此,本文提出了一种解决方案,利用DU的最新进展来设计一种创新的身份文件识别(IDR)策略,即旨在自动理解、提取和转录身份证字段的任务。我们设计了一种两阶段的方法,基于对合成数据和真实数据的预训练模型进行微调。我们还开发了一个专用的合成数据生成工具来支持IDR过程。实验结果证明了该方法的有效性。
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CiteScore
4.50
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