Deep TextSpotter: An End-to-End Trainable Scene Text Localization and Recognition Framework

By M. Busta, Lukás Neumann, Jiri Matas. 2017

240 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. Minghui Liao
4. Pengyuan Lyu
5. Xin He
6. Chunhua Shen
7. Shangbang Long
8. Baoguang Shi

In Media Technology

1. Cong Yao
2. Xiang Bai
3. Minghui Liao
4. Xin He
5. Shangbang Long
6. Pengyuan Lyu
7. Chengquan Zhang
8. Baoguang Shi

Citations in the corpus

• All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting (2019, methodology): “Methods in (Liu et al. 2018; He et al. 2018; Busta et al. 2017; Sun et al. 2018) extract text features in the minimum oriented rectangular box, which can tackle oriented text.”
• Scene Text Visual Question Answering (2019, background): “Later works focus towards end-to-end architectures such as the ones presented by [8, 39, 20], which mostly con-”
• Scene Text Detection and Recognition: The Deep Learning Era (2018, background): “Liu et al. (2018c) and Busta et al. (2017) adopt EAST (Zhou et al. 2017) and YOLOv2 (Redmon and Farhadi 2017) as their detection branches respectively, and have a similar text recognition branch in which text proposals are pooled into fixed height tensors by bilinear sampling and then transcribe…”
• TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network (2018, methodology): “nd-to-end Word spotting S W G S W G Deep2Text II+ 81.81 79.49 76.99 84.84 83.43 78.90 TextBoxes [27] 91.57 89.65 83.89 93.90 91.95 85.92 Li et al. [25] 91.08 89.81 84.59 94.16 92.42 88.20 TextSpotter [5] 89.0 86.0 77.0 92.0 89.0 81.0 TextNet 89.77 88.80 82.96 94.59 93.48 86.99 12 Y. Sun, C. Zhang et”
• Multi-oriented Scene Text Detection via Corner Localization and Region Segmentation (2018, background): “Scene text detection, which aims at locating text in natural images, plays an important role in various text reading systems [35, 11, 50, 6, 21, 52, 5, 42, 41, 14, 8, 26].”
• Rotation-Sensitive Regression for Oriented Scene Text Detection (2018, background): “Recently, numerous inspiring ideas and promising methods [48, 49, 43, 19, 50, 11, 44, 26, 51, 38, 29, 13, 12, 14, 46, 5, 24, 42] have been proposed.”
• ESIR: End-To-End Scene Text Recognition via Iterative Image Rectification (2018, unknown): “[13], [6] and [36] propose end-to-end systems that use RNNs for visual feature representation and CTC for sequence prediction.”
• Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes (2018, methodology): “The left presents horizontal text spotting methods [30, 27]; The middle indicates oriented text spotting methods [3]; The right is our proposed method.”
• ASTER: An Attentional Scene Text Recognizer with Flexible Rectification (2019, methodology): “For example, TextBoxes [38] adapts the SSD detector [39] to text detection.”