Rosetta: Large scale system for text detection and recognition in images
This addresses the need for understanding textual information in images on social media platforms like Facebook and Instagram to improve search and recommendation applications, though it is incremental as it builds on existing OCR techniques.
The authors tackled the problem of detecting and recognizing text in images at Facebook scale, presenting Rosetta, a deployed OCR system that processes daily image uploads efficiently, with evaluation showing practical scalability and performance insights.
In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook and Instagram, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.