Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees

By Weilin Huang, Yu Qiao, Xiaoou Tang. 2014

395 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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. Xiang Bai
2. Cong Yao
3. Chunhua Shen
4. Luc Van Gool
5. Dacheng Tao
6. Yu Qiao
7. Ming-Hsuan Yang
8. Xiaogang Wang

In Media Technology

1. Cong Yao
2. Xiang Bai
3. Minghui Liao
4. Xin He
5. Shangbang Long
6. Lianwen Jin
7. Yuliang Liu
8. Pengyuan Lyu

Citations in the corpus

• Recent advances in convolutional neural networks (2015, background): “Another work that combines MSER and CNN for text detection is [157].”
• Synthetic Data for Text Localisation in Natural Images (2016, neutral): “Huang [17] - - - - - - - - - - - - 78 88 71 - - - - - -”
• Arbitrary-Oriented Scene Text Detection via Rotation Proposals (2017, background): “In the past years there have been many attentions in the text detection task [3, 22, 7, 35, 23, 1, 15, 12, 31].”
• Detecting Oriented Text in Natural Images by Linking Segments (2017, background): “Character-based methods [17, 23, 24, 10, 7, 8] detect individual characters and group them into words.”
• A Fast Uyghur Text Detector for Complex Background Images (2018, methodology): “He, R. Girshick, and J. Sun, “Faster R-CNN: Towards realtime object detection with region proposal networks,” IEEE Trans.”
• OCR-Free Document Understanding Transformer (2021, methodology): “Early detection methods used CNNs to predict local segments and apply heuristics to merge them [19,69].”
• Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes (2018, background): “A large number of methods have been proposed to detect scene text [7, 36, 37, 50, 19, 23, 54, 21, 47, 54, 56, 30, 52, 55, 34, 15, 48, 43, 57, 16, 35, 31].”
• Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition (2017, methodology): “[6] trained a character classifier to examine components generated by MSER, with the objective of improving the robustness of feature extraction process.”
• TextBoxes: A Fast Text Detector with a Single Deep Neural Network (2016, neutral): “…on a basic detection target, previous methods for text detection can be roughly categorized into three categories: 1) Character-based: Individual characters are first detected and then grouped into words (Neumann and Matas 2012; Pan, Hou, and Liu 2011; Yao et al. 2012; Huang, Qiao, and Tang 2014).”
• Robust Seed Localization and Growing with Deep Convolutional Features for Scene Text Detection (2015, methodology): “In [3], CNN is used to predict and assign a confident value to each MSER component, which is exploited to both localize text components to be aggregated into text-lines and, with information of MSERs trees, help to identify and split error-connected components.”
• TextBoxes++: A Single-Shot Oriented Scene Text Detector (2018, background): “The works in [21], [28]–[30] are such examples.”
• 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”
• Multi-oriented Text Detection with Fully Convolutional Networks (2016, methodology): “By considering both of the two level cues at the same time, our approach has two advantages compared to component based methods [16, 4, 6].”
• EAST: An Efficient and Accurate Scene Text Detector (2017, methodology): “[11] first found candidates using MSER and then employed a deep convolutional network as a strong classifier to prune false positives.”