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International Journal of Computer Vision

Bottom-Up Scene Text Detection with Markov Clustering Networks

  • 10-02-2020
Published in:
Authors
Zichuan Liu
Guosheng Lin
Wang Ling Goh
Published in
International Journal of Computer Vision | Issue 6/2020

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Abstract

A novel detection framework named Markov Clustering Network (MCN) is proposed for fast and robust scene text detection. Different from the traditional top-down scene text detection approaches that inherit from the classic object detection, MCN detects scene text objects in a bottom-up manner. MCN predicts instance-level bounding boxes by firstly converting an image into a stochastic flow graph where Markov Clustering is performed based on the predicted stochastic flows. The stochastic flows encode the local correlation and semantic information of scene text objects. An object is modeled as strongly connected nodes by flows, which allows flexible and bottom-up detection for scale-varying and rotated text objects without prior knowledge of object size. The flow prediction is supported by the advanced Convolutional Neural Networks architectures and Position-aware spatial attention mechanism, which provides enhanced flow prediction by adaptively fusing spatial representations. The experimental evaluation on public benchmarks shows that our MCN method achieves the state-of-art performance on public benchmarks, especially in retrieving long and oriented texts.

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previous article RGB-IR Person Re-identification by Cross-Modality Similarity Preservation
next article Multi-task Generative Adversarial Network for Detecting Small Objects in the Wild
Title
Bottom-Up Scene Text Detection with Markov Clustering Networks
Authors
Zichuan Liu
Guosheng Lin
Wang Ling Goh
Publication date
10-02-2020
Publisher
Springer US
Published in
International Journal of Computer Vision / Issue 6/2020
Print ISSN: 0920-5691
Electronic ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-020-01298-y
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