research-article

AB-LSTM: Attention-based Bidirectional LSTM Model for Scene Text Detection

Authors:

Houqiang LiAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 15, Issue 4

Article No.: 107, Pages 1 - 23

https://doi.org/10.1145/3356728

Published: 16 December 2019 Publication History

Abstract

Detection of scene text in arbitrary shapes is a challenging task in the field of computer vision. Most existing scene text detection methods exploit the rectangle/quadrangular bounding box to denote the detected text, which fails to accurately fit text with arbitrary shapes, such as curved text. In addition, recent progress on scene text detection has benefited from Fully Convolutional Network. Text cues contained in multi-level convolutional features are complementary for detecting scene text objects. How to explore these multi-level features is still an open problem. To tackle the above issues, we propose an Attention-based Bidirectional Long Short-Term Memory (AB-LSTM) model for scene text detection. First, word stroke regions (WSRs) and text center blocks (TCBs) are extracted by two AB-LSTM models, respectively. Then, the union of WSRs and TCBs are used to represent text objects. To verify the effectiveness of the proposed method, we perform experiments on four public benchmarks: CTW1500, Total-text, ICDAR2013, and MSRA-TD500, and compare it with existing state-of-the-art methods. Experiment results demonstrate that the proposed method can achieve competitive results, and well handle scene text objects with arbitrary shapes (i.e., curved, oriented, and horizontal forms).

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Index Terms

AB-LSTM: Attention-based Bidirectional LSTM Model for Scene Text Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Interest point and salient region detections
      2. Computer vision tasks
        Scene understanding

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 15, Issue 4

November 2019

322 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3376119

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2019 ACM.

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Publication History

Published: 16 December 2019

Accepted: 01 August 2019

Revised: 01 August 2019

Received: 01 December 2018

Published in TOMM Volume 15, Issue 4

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Youth Innovation Promotion Association of the Chinese Academy of Sciences
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