Total-Text: toward orientation robustness in scene text detection

By Chee-Kheng Chng, Chee Seng Chan, Chenglin Liu. 2019

138 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. Chunhua Shen
2. Xiang Bai
3. Anton van den Hengel
4. Luc Van Gool
5. Qi Wu
6. Dacheng Tao
7. Xiaogang Wang
8. Peng Wang

In Media Technology

1. Chunhua Shen
2. Lianwen Jin
3. Xiang Bai
4. Yuliang Liu
5. Mingkun Yang
6. Tong He
7. Yongchao Xu
8. Peng Wang

Citations in the corpus

• All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting (2019, background): “SynthText (Gupta, Vedaldi, and Zisserman 2016) has about 800,000 images, which are generated via synthesizing engine.”
• Structured Multimodal Attentions for TextVQA (2020, methodology): “The SBD model is pretrained on a 60k dataset, which consists of 30, 000 images from LSVT [40] training set, 10, 000 images from MLT 2019 [31] training set, 5, 603 images from ArT [10] which contains all the images of SCUT-CTW1500 [27] and Total-text [9,11], and the rest of 14, 859 images are selecte”
• On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering (2020, background): “collected a total of 20,757 images from publicly available scene text detection and recognition datasets. Specifically, images annotated with English questions and answers are obtained from Total-Text [5], ICDAR 2013 [14], ICDAR 2015 [13], CTW1500 [20], MLT [25], and COCO Text [32]. Whereas, images w”
• ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network (2020, background): “Its extended version [4] improves its annotation of training set by annotating each text instance with a fixed ten points following text recognition sequence.”
• ChatGPT and Open-AI Models: A Preliminary Review (2023, background): “Difficulty with non-standard English: ChatGPT has been trained primarily on standard English text and may have difficulty with non-standard varieties of English, such as regional dialects or slang [85].”