text Detection with Convolutional Neural Networks

By Manolis Delakis, Christophe Garcia. 2008

80 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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. G. Wang
2. Jianfei Cai
3. Luc Van Gool
4. Ming-Hsuan Yang
5. Yi Yang
6. Dacheng Tao
7. Xiaogang Wang
8. Jitendra Malik

Citations in the corpus

• Recent advances in convolutional neural networks (2015, methodology): “1) Text detection: One of the pioneering works to apply CNN for scene text detection is [155].”
• Convolutional networks and applications in vision (2010, background): “Other experimental detection applications include hands/gesture [19], logos and text [20].”
• Large-Scale FPGA-based Convolutional Networks (2011, methodology): “…(Frome et al., 2009), or customers’ gender and age at NEC, • more experimental detection of hands/gestures (Nowlan and Platt, 1995), logos and text (Delakis and Garcia, 2008), • vision-based navigation for off-road robots: in the DARPA-sponsored LAGR program, ConvNets were used for…”
• ShiDianNao: Shifting vision processing closer to the sensor (2015, unknown): “92 ConvNN [9] 45.”
• Learning fine-grained features via a CNN Tree for Large-scale Classification (2015, background): “Convolutional neural networks (CNN) [1], [2] have recently demonstrated superior performance on many tasks such as image classification [3], [4], [5], object detection [6], [7], [8], [9], [10], [11], object tracking [12], [13], [14], text detection [15], [16], text recognition [17], [18], [19], vide”
• Text Detection and Recognition in Imagery: A Survey (2015, methodology): “Text detection is performed with a convolutional neural network [67] trained on raw pixel”
• Simplifying ConvNets for Fast Learning (2012, background): “[1], have shown great performances in various computer vision applications, such as handwritten character recognition [1, 2], facial analysis [3–5], videoOCR [6, 7], or vision-based navigation [8].”