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Chengquan Zhang

Researcher at Baidu

Publications -  46
Citations -  2691

Chengquan Zhang is an academic researcher from Baidu. The author has contributed to research in topics: Computer science & Character (mathematics). The author has an hindex of 16, co-authored 39 publications receiving 1845 citations. Previous affiliations of Chengquan Zhang include Huazhong University of Science and Technology.

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Proceedings ArticleDOI

The Seventh Visual Object Tracking VOT2019 Challenge Results

Matej Kristan, +179 more
TL;DR: The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative; results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Proceedings ArticleDOI

Multi-oriented Text Detection with Fully Convolutional Networks

TL;DR: A novel approach for text detection in natural images that consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, I CDAR2015 and ICDAR2013.
Posted Content

Multi-Oriented Text Detection with Fully Convolutional Networks

TL;DR: In this article, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner, and text line hypotheses are estimated by combining the saliency map and character components.
Proceedings ArticleDOI

Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes

TL;DR: Li et al. as discussed by the authors proposed a text detector named LOMO, which consists of a direct regressor (DR), an iterative refinement module (IRM), and a shape expression module (SEM).
Proceedings ArticleDOI

Towards Accurate Scene Text Recognition With Semantic Reasoning Networks

TL;DR: Zhang et al. as discussed by the authors proposed a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission.