10 citations · 18 across the 5 of their papers we have counts for
10 papers
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…
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…
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…
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…
When and How Can Deep Generative Models be Inverted?
Aviad Aberdam, Dror Simon, Michael Elad
Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.…
Ada-LISTA: Learned Solvers Adaptive to Varying Models
Aviad Aberdam, Alona Golts, Michael Elad
Neural networks that are based on unfolding of an iterative solver, such as LISTA (learned iterative soft threshold algorithm), are widely used due to their accelerated performance…