To show or not to show: Redacting sensitive text from videos of electronic displays
This addresses privacy concerns in video recordings, but it is incremental as it compares existing OCR models rather than introducing a new method.
The paper tackled the problem of redacting personally identifiable text from videos to maintain privacy, using OCR and NLP techniques, and found that Google Cloud Vision significantly outperformed Tesseract in accuracy and speed.
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.