# To show or not to show: Redacting sensitive text from videos of electronic displays

arXiv: https://arxiv.org/abs/2208.10270 · HTML: https://mlsanity.com/paper/2208.10270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Learning to Anonymize Faces for Privacy Preserving Action Detection](https://mlsanity.com/paper/1803.11556/) (2018)
- [Live Face De-Identification in Video](https://mlsanity.com/paper/1911.08348/) (2019)
- [On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing](https://mlsanity.com/paper/2011.08019/) (2020)
- [CenterFace: Joint Face Detection and Alignment Using Face as Point](https://mlsanity.com/paper/1911.03599/) (2019)

## Abstract (arXiv)

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.