Using text-spotting to query the world

By Ingmar Posner, Peter Corke, Paul M. Newman. 2010

47 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Extraction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Andrea Vedaldi
2. Andrew Zisserman
3. Luc Van Gool
4. Dacheng Tao
5. Jitendra Malik
6. Ming-Hsuan Yang
7. Xiaogang Wang
8. Trevor Darrell

In Information Extraction

1. Andrea Vedaldi
2. Andrew Zisserman
3. Max Jaderberg

Citations in the corpus

• Speeding up Convolutional Neural Networks with Low Rank Expansions (2014, background): “Character classification is an essential part of many text spotting pipelines such as [3, 4, 15, 24, 25, 26, 27, 29, 30, 41, 43].”
• Reading Text in the Wild with Convolutional Neural Networks (2014, background): “2014), or on combining both in end-to-end systems (Alsharif and Pineau 2014; Gordo 2014; Jaderberg et al. 2014; Mishra et al. 2013; Neumann and Matas 2010, 2011, 2012, 2013; Posner et al. 2010; Quack 2009; Wang et al. 2011, 2012; Weinman et al. 2014).”
• Deep Features for Text Spotting (2014, methodology): “Text detection methods are either based on connected components (CCs) [11, 16, 50, 49, 32–35] or sliding windows [40, 7, 39, 45].”