Texts as Lines: Text Detection with Weak Supervision

Weijia Wu, Jici Xing, Cheng Yang, Yuxing Wang and Hong Zhou

Mathematical Problems in Engineering, 2020, vol. 2020, 1-12

Abstract:

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.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2020/3871897.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2020/3871897.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3871897

DOI: 10.1155/2020/3871897

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().