Skip to main content

Research Repository

Advanced Search

A text-context-aware CNN network for multi-oriented and multi-language scene text detection

Xiao, Yao; Xue, Minglong; Lu, Tong; Wu, Yirui; Palaiahnakote, Shivakumara

Authors

Yao Xiao

Minglong Xue

Tong Lu

Yirui Wu



Abstract

The existing deep learning based state-of-theart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on arbitrary orientation and large aspect ratios of scene texts based on instance segmentation algorithms. However, due to the lack of context information with consideration of scene text unique characteristics, directly applying instance segmentation to text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a novel text-context-aware scene text detection CNN structure, which appropriately encodes channel and spatial attention information to construct context-aware and discriminative feature map for multi-oriented and multi-language text detection tasks. With high representation ability of text context-aware feature map, the proposed instance segmentation based method can not only robustly detect multi-oriented and multi-language text from natural scene images, but also produce better text detection results by greatly reducing false positives. Experiments on ICDAR2015 and ICDAR2017-MLT datasets show that the proposed method has achieved superior performances in precision, recall and F-measure than most of the existing studies.

Presentation Conference Type Conference Paper (published)
Conference Name 2019 International Conference on Document Analysis and Recognition (ICDAR)
Start Date Sep 20, 2019
End Date Sep 25, 2019
Online Publication Date Feb 3, 2020
Publication Date Feb 3, 2020
Deposit Date Nov 15, 2024
Publisher Institute of Electrical and Electronics Engineers
Series ISSN 2379-2140
Book Title 2019 International Conference on Document Analysis and Recognition (ICDAR)
ISBN 9781728128610
DOI https://doi.org/10.1109/ICDAR.2019.00116