  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.
