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.
2 code implementations • CVPR 2020 • Ron Litman, Oron Anschel, Shahar Tsiper, Roee Litman, Shai Mazor, R. Manmatha
The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer.
1 code implementation • CVPR 2018 • Simon Korman, Roee Litman
We present a method that can evaluate a RANSAC hypothesis in constant time, i. e. independent of the size of the data.
no code implementations • CVPR 2017 • Matthias Vestner, Roee Litman, Emanuele Rodolà, Alex Bronstein, Daniel Cremers
Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space.
no code implementations • 12 Jul 2016 • Matthias Vestner, Roee Litman, Alex Bronstein, Emanuele Rodolà, Daniel Cremers
Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space.
no code implementations • NeurIPS 2013 • Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M. Bronstein, Guillermo Sapiro
In this paper, we propose a new and computationally efficient framework for learning sparse models.
no code implementations • CVPR 2015 • Roee Litman, Simon Korman, Alexander Bronstein, Shai Avidan
This work presents a novel approach for detecting inliers in a given set of correspondences (matches).
no code implementations • 1 Mar 2020 • David Pickup, Xianfang Sun, Paul L. Rosin, Ralph R. Martin, Z Cheng, Zhouhui Lian, Masaki Aono, A. Ben Hamza, A Bronstein, M Bronstein, S Bu, Umberto Castellani, S Cheng, Valeria Garro, Andrea Giachetti, Afzal Godil, Luca Isaia, J. Han, Henry Johan, L Lai, Bo Li, C. Li, Haisheng Li, Roee Litman, X. Liu, Z Liu, Yijuan Lu, L. Sun, G Tam, Atsushi Tatsuma, J. Ye
In addition, further participants have also taken part, and we provide extra analysis of the retrieval results.
no code implementations • ECCV 2020 • Amir Markovitz, Inbal Lavi, Or Perel, Shai Mazor, Roee Litman
We present CREASE: Content Aware Rectification using Angle Supervision, the first learned method for document rectification that relies on the document's content, the location of the words and specifically their orientation, as hints to assist in the rectification process.
Optical Character Recognition Optical Character Recognition (OCR)