TextBFA: Arbitrary Shape Text Detection with Bidirectional Feature Aggregation

Hui Xu*, Qiu Feng Wang, Zhenghao Li, Yu Shi, Xiang Dong Zhou

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Scene text detection has achieved great progress recently, however, it is challenging to detect arbitrary shaped text in the scene images with complex background, especially for those unobvious and long texts. To tackle this issue, we propose an effective text detection network, termed TextBFA, strengthening the text feature by aggregating high-level semantic features. Specifically, we first adopt a bidirectional feature aggregation network to propagate and collect information on feature maps. Then, we exploit a bilateral decoder with lateral connection to recover the low-resolution feature maps for pixel-wise prediction. Extensive experiments demonstrate the detection effectiveness of the proposed method on several benchmark datasets, especially on inconspicuous text detection.

Original languageEnglish
Title of host publicationNeural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
EditorsBiao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9789819981311
Publication statusPublished - 2024
Event30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China
Duration: 20 Nov 202323 Nov 2023

Publication series

NameCommunications in Computer and Information Science
Volume1962 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference30th International Conference on Neural Information Processing, ICONIP 2023


  • Scene text detection
  • feature aggregation
  • inconspicuous text


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