Automatic discrimination of text and non-text natural images

By Chengquan Zhang, Cong Yao, Baoguang Shi, Xiang Bai. 2015

24 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. Jianfei Cai
2. G. Wang
3. Xiang Bai
4. Jiuxiang Gu
5. Baoguang Shi
6. Chengquan Zhang
7. Cong Yao
8. Amir Shahroudy

Citations in the corpus

• Recent advances in convolutional neural networks (2015, methodology): “In [158], text candidates are obtained using MSER which are then passed into a CNN to generate visual features, and lastly the global features of the images are constructed by aggregating the CNN features in a Bag-ofWords (BoW) framework.”
• Text/non-text image classification in the wild with convolutional neural networks (2017, methodology): “To our knowledge, our previous work [32] first proposed a suitable method that is the combination of three mature techniques including: MSERs, BoW, and CNN for text/non-text image classification.”