Nguyen, Thi Ngoc Diep
(2019)
State-of-the-Art in Action: Unconstrained Text Detection.
In: the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Real-World Recognition from Low-Quality images and Video (RLQ@ICCV’19), October 27, 2019, Seoul, Republic of Korea.
Abstract
In this paper, we stage five real-world scenarios for six
state-of-the-art text detection methods in order to evaluate
how competent they are with new data without any training process. Moreover, this paper analyzes the architecture
design of those methods to reveal the influence of pipeline
choices on the detection quality. The setup of experimental studies are straight-forward: we collect and manually
annotate test data, we reimplement the pretrained models
of the state-of-the-art methods, then we evaluate and analyze how well each method achieve in each of our collected
datasets. We found that most of the state-of-the-art methods
are competent at detecting textual information in unseen
data, however, some are more readily used for real-world
applications. Surprisingly, we also found that the choice
of a post-processing algorithm correlates strongly with the
performance of the corresponding method. We expect this
paper would serve as a reference for researchers as well as
application developers in the field.
All collected data with ground truth annotation and
their detected results is publicly available at our Github
repository: https://github.com/chupibk/
HBlab-rlq19.
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