Text Detection and Recognition in Imagery: A Survey

By Qixiang Ye, David S. Doermann. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015

755 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/caec97674544a4948a1b0ec2b9f6c624b87b647b

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. Dacheng Tao
5. Luc Van Gool
6. Yu Qiao
7. Minghui Liao
8. Andrew Zisserman

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

• Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks (2017, background): “There are comprehensive surveys for text detection and recognition in [30, 36].”
• Imbalance Problems in Object Detection: A Review (2019, neutral): “pedestrian detection, vehicle detection, face detection) [34], [35], [36], [37].”
• Symmetry-based text line detection in natural scenes (2015, neutral): “For comprehensive surveys, refer to [19, 44, 50].”
• Multi-oriented Text Detection with Fully Convolutional Networks (2016, background): “Comprehensive surveys for scene text detection and recognition can be referred to [30, 36].”
• Scene Text Detection and Recognition: The Deep Learning Era (2018, background): “There have been already several excellent review papers [136], [154], [160], [172], which also comb and analyze works related text detection and recognition.”
• Object Detection in 20 Years: A Survey (2019, background): “The disadvantage is the inference will be computationally expensive when considering a large number of character classes and candidate windows [339].”
• TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes (2018, neutral): “For comprehensive surveys, please refer to [41, 48].”
• Arbitrary-Oriented Scene Text Detection via Rotation Proposals (2017, background): “The reading of text in the wild has been studied over the last few decades; comprehensive surveys can be found in [24]–[27].”
• Recognizing Text-Based Traffic Guide Panels with Cascaded Localization Network (2016, methodology): “Most state-of-the-art methods of scene text extraction [1,2,4,5,6,7,8,9,10] comprise two stages, detection to obtain image regions containing text information, and recognition to transform image-based text information into text codes.”
• Text-Attentional Convolutional Neural Network for Scene Text Detection (2015, background): “Existing work for scene text detection can be roughly categorized into two groups, sliding-window and connected component based methods [11].”
• Robust Scene Text Recognition with Automatic Rectification (2016, neutral): “Comprehensive surveys have been given in [40, 44].”
• ASTER: An Attentional Scene Text Recognizer with Flexible Rectification (2019, background): “Comprehensive surveys can be found in [68], [72].”
• Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection (2017, background): “However, Ye and Doermann [34] indicated that because of characters distortion, the boundary of text may lose rectangular shape, and the rectangular constraints may result in redun-1 dant background noise, unnecessary overlap or even information loss when detecting distorted incidental scene text as…”
• PixelLink: Detecting Scene Text via Instance Segmentation (2018, background): “Reading text in the wild, or robust reading has drawn great interest for a long time (Ye and Doermann 2015).”
• Towards Large-Scale Small Object Detection: Survey and Benchmarks (2022, background): “Quite a number of surveys about object detection have been published in recent years [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], and our review differs from the existing ones mainly in two aspects.”
• Focusing Attention: Towards Accurate Text Recognition in Natural Images (2017, background): “For general information of text recognition, the readers can refer to Ye and Doermann’s recent survey [33].”