Application of Deep Learning in Recognizing Bates Numbers and Confidentiality Stamping from Images
This work addresses the problem of manual and laborious quality control in legal document productions for eDiscovery professionals, aiming to automate the validation of Bates Numbers and confidentiality stamps.
This paper introduces an automated quality control application for eDiscovery that uses deep learning to recognize Bates Numbers and confidentiality stamps on legal document images. The system then validates their correctness, aiming to reduce manual effort and errors in legal productions. The method's effectiveness was verified using real-world production data.
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.