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20202022
most citedMultimodal Semi-Supervised Learning for Text Recognition

10 citations · 12 across the 4 of their papers we have counts for

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8 papers · 1 filter

cs.CV2022

Out-of-Vocabulary Challenge Report

Sergi Garcia-Bordils, Andrés Mafla, Ali Furkan Biten +5

This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Re…

cs.CV202210 cited

Multimodal Semi-Supervised Learning for Text Recognition

Aviad Aberdam, Roy Ganz, Shai Mazor +1

Until recently, the number of public real-world text images was insufficient for training scene text recognizers. Therefore, most modern training methods rely on synthetic data and…

cs.CV20211 cited

Learning Multimodal Affinities for Textual Editing in Images

Or Perel, Oron Anschel, Omri Ben-Eliezer +2

Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent. Unlike natural images that capture physical objects, do…

cs.CV20201 cited

On Calibration of Scene-Text Recognition Models

Ron Slossberg, Oron Anschel, Amir Markovitz +6

In this work, we study the problem of word-level confidence calibration for scene-text recognition (STR). Although the topic of confidence calibration has been an active research a…

cs.CV2020

Sequence-to-Sequence Contrastive Learning for Text Recognition

Aviad Aberdam, Ron Litman, Shahar Tsiper +5

We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence…

cs.CV2020

Can You Read Me Now? Content Aware Rectification using Angle Supervision

Amir Markovitz, Inbal Lavi, Or Perel +2

The ubiquity of smartphone cameras has led to more and more documents being captured by cameras rather than scanned. Unlike flatbed scanners, photographed documents are often folde…