  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.
