Texts as Lines: Text Detection with Weak Supervision
Joint Authors
Wu, Weijia
Xing, Jici
Yang, Cheng
Wang, Yuxing
Zhou, Hong
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-10
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Scene text detection methods based on deep learning have recently shown remarkable improvement.
Most text detection methods train deep convolutional neural networks with full masks requiring pixel accuracy for good quality training.
Normally, a skilled engineer needs to drag tens of points to create a full mask for the curved text.
Therefore, data labelling based on full masks is time consuming and laborious, particularly for curved texts.
To reduce the labelling cost, a weakly supervised method is first proposed in this paper.
Unlike the other detectors (e.g., PSENet or TextSnake) that use full masks, our method only needs coarse masks for training.
More specifically, the coarse mask for one text instance is a line across the text region in our method.
Compared with full mask labelling, data labelling using the proposed method could save labelling time while losing much annotation information.
In this context, a network pretrained on synthetic data with full masks is used to enhance the coarse masks in a real image.
Finally, the enhanced masks are fed back to train our network.
Analysis of experiments performed using the model shows that the performance of our method is close to that of the fully supervised methods on ICDAR2015, CTW1500, Total-Text, and MSRA-TD5000.
American Psychological Association (APA)
Wu, Weijia& Xing, Jici& Yang, Cheng& Wang, Yuxing& Zhou, Hong. 2020. Texts as Lines: Text Detection with Weak Supervision. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1194697
Modern Language Association (MLA)
Wu, Weijia…[et al.]. Texts as Lines: Text Detection with Weak Supervision. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1194697
American Medical Association (AMA)
Wu, Weijia& Xing, Jici& Yang, Cheng& Wang, Yuxing& Zhou, Hong. Texts as Lines: Text Detection with Weak Supervision. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1194697
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194697