13 citations · 14 across the 3 of their papers we have counts for
6 papers · 1 filter
Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer
Yair Kittenplon, Inbal Lavi, Sharon Fogel +3
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. Existing…
TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers
Oren Nuriel, Sharon Fogel, Ron Litman
Leveraging the characteristics of convolutional layers, neural networks are extremely effective for pattern recognition tasks. However in some cases, their decisions are based on u…
Single Pair Cross-Modality Super Resolution
Guy Shacht, Sharon Fogel, Dov Danon +2
Non-visual imaging sensors are widely used in the industry for different purposes. Those sensors are more expensive than visual (RGB) sensors, and usually produce images with lower…
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen +2
Optical character recognition (OCR) systems performance have improved significantly in the deep learning era. This is especially true for handwritten text recognition (HTR), where…
Blind Visual Motif Removal from a Single Image
Amir Hertz, Sharon Fogel, Rana Hanocka +2
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. For example, deco…
Clustering-driven Deep Embedding with Pairwise Constraints
Sharon Fogel, Hadar Averbuch-Elor, Jacov Goldberger +1
Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have b…