English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Though deep learning based scene text detection has achieved great progress,well-trained detectors suffer from severe performance degradation for differentdomains. In general, a tremendous amount of data is indispensable to train thedetector in the target domain. However, data collection and annotation areexpensive and time-consuming. To address this problem, we propose aself-training framework to automatically mine hard examples with pseudo-labelsfrom unannotated videos or images. To reduce the noise of hard examples, anovel text mining module is implemented based on the fusion of detection andtracking results. Then, an image-to-video generation method is designed for thetasks that videos are unavailable and only images can be used. Experimentalresults on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT,demonstrate the effectiveness of our self-training method. The simple MaskR-CNN adapted with self-training and fine-tuned on real data can achievecomparable or even superior results with the state-of-the-art methods.

Self-Training for Domain Adaptive Scene Text Detection