Application of Deep Learning in Recognizing Bates Numbers and Confidentiality Stamping from Images

TL;DR

This paper presents a deep learning-based image recognition system to automatically verify Bates Numbers and Confidentiality Stamps in legal document images, improving quality control in eDiscovery processes.

Contribution

It introduces a novel automated approach leveraging deep learning for extracting and validating legal document markings, enhancing efficiency over manual methods.

Findings

High accuracy in recognizing Bates Numbers and Confidentiality Stamps

Significant reduction in manual quality control effort

Validated effectiveness with real-world legal data

Abstract

In eDiscovery, it is critical to ensure that each page produced in legal proceedings conforms with the requirements of court or government agency production requests. Errors in productions could have severe consequences in a case, putting a party in an adverse position. The volume of pages produced continues to increase, and tremendous time and effort has been taken to ensure quality control of document productions. This has historically been a manual and laborious process. This paper demonstrates a novel automated production quality control application which leverages deep learning-based image recognition technology to extract Bates Number and Confidentiality Stamping from legal case production images and validate their correctness. Effectiveness of the method is verified with an experiment using a real-world production data.