Search Results for author: Sharon Fogel

Found 6 papers, 3 papers with code

Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer

no code implementations11 Feb 2022 Yair Kittenplon, Inbal Lavi, Sharon Fogel, Yarin Bar, R. Manmatha, Pietro Perona

Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components.

Text Spotting

TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers

no code implementations9 May 2021 Oren Nuriel, Sharon Fogel, Ron Litman

Motivated by this, we suggest an approach to regulate the reliance on local statistics that improves text recognition performance.

Handwritten Text Recognition Scene Text Recognition

Single Pair Cross-Modality Super Resolution

no code implementations CVPR 2021 Guy Shacht, Sharon Fogel, Dov Danon, Daniel Cohen-Or, Ilya Leizerson

The network is trained on the two input images only, learns their internal statistics and correlations, and applies them to up-sample the target modality.


ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation

3 code implementations CVPR 2020 Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor, Roee Litman

This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design.

Domain Adaptation Handwriting generation +3

Blind Visual Motif Removal from a Single Image

1 code implementation CVPR 2019 Amir Hertz, Sharon Fogel, Rana Hanocka, Raja Giryes, Daniel Cohen-Or

Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image.

Clustering-driven Deep Embedding with Pairwise Constraints

1 code implementation22 Mar 2018 Sharon Fogel, Hadar Averbuch-Elor, Jacov Goldberger, Daniel Cohen-Or

In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network.

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