@inproceedings{23a0cea1be2b460a99ab3b50312549b7,
title = "TextBFA: Arbitrary Shape Text Detection with Bidirectional Feature Aggregation",
abstract = "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.",
keywords = "Scene text detection, feature aggregation, inconspicuous text",
author = "Hui Xu and Wang, \{Qiu Feng\} and Zhenghao Li and Yu Shi and Zhou, \{Xiang Dong\}",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 30th International Conference on Neural Information Processing, ICONIP 2023 ; Conference date: 20-11-2023 Through 23-11-2023",
year = "2024",
doi = "10.1007/978-981-99-8132-8\_28",
language = "English",
isbn = "9789819981311",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "365--377",
editor = "Biao Luo and Long Cheng and Zheng-Guang Wu and Hongyi Li and Chaojie Li",
booktitle = "Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings",
}