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Rosetta: Large scale system for text detection and recognition in images

About

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.

Fedor Borisyuk, Albert Gordo, Viswanath Sivakumar• 2019

Related benchmarks

TaskDatasetResultRank
Scene Text RecognitionIC15 (test)
Word Accuracy86.1
210
Scene Text RecognitionIC13 (test)
Word Accuracy96.3
207
Scene Text RecognitionIIIT5K
Accuracy84.3
149
Scene Text RecognitionSVT 647 (test)
Accuracy92.3
101
Scene Text RecognitionCUTE
Accuracy69.2
92
Scene Text RecognitionIC15
Accuracy71.2
86
Scene Text RecognitionIC03
Accuracy92.9
67
Scene Text RecognitionSVT
Accuracy84.7
67
Scene Text RecognitionIC13
Accuracy89
66
Scene Text RecognitionSVTP 645 (test)
Accuracy86.2
54
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