Role of AI in Quality Checking of Captions 

In today’s ultra-competitive media and entertainment industry, captioning demands exceptional precision. However, when quality control (QC) is conducted manually, it is very labor-intensive and prone to errors, which can lead to compromises in quality. For example, the QC process of ensuring adherence to Federal Communications Commission (FCC) guidelines — specifically, those relating to sync, accuracy, and completeness — requires multiple reviews. Furthermore, it involves verification of segmentation, reading speed, display duration, and layout metrics like row and column count. Caption placement also needs careful adjustment to avoid obstructing important visual elements, while global deliveries necessitate multilingual quality checks to meet diverse audience standards. Additionally, profanity censoring is critical. With all these requirements, performing general checks — such as ensuring captions aren’t delayed during critical or suspenseful moments in a scene — can often be overlooked. This paper will explore how Artificial Intelligence (AI) is streamlining these complex QC tasks and freeing up human resources, enabling media companies to focus on the more creative aspects of their workflows.

Manik Gupta, Sana Afsar, Jeff Ross | Interra Systems | Cupertino, Calif., United States