  With the increasing prevalence of video recordings there is a growing need
for tools that can maintain the privacy of those recorded. In this paper, we
define an approach for redacting personally identifiable text from videos using
a combination of optical character recognition (OCR) and natural language
processing (NLP) techniques. We examine the relative performance of this
approach when used with different OCR models, specifically Tesseract and the
OCR system from Google Cloud Vision (GCV). For the proposed approach the
performance of GCV, in both accuracy and speed, is significantly higher than
Tesseract. Finally, we explore the advantages and disadvantages of both models
in real-world applications.
