Natural scene text detection based on enhanced multi-level feature fusion
ZHOU Yan
WEI Qinbin
LIAO Junwei
ZENG Fanzhi
LIU Xiangyu
ZHOU Yuexia
Abstract：Aiming at the detection problems caused by unfocused small text,complex background text and wide-spaced curved text in natural scene images,a natural scene text detection method based on enhanced multi-level feature fusion was proposed.It includes the Local Attention Feature Enhanced(LAFE)module and the Multi-level Enhanced Feature Fused(MEFF)module.LAFE module expands the sensory field of the network by stacking dilated convolution and enhances the classification ability of pixels by combining channels and spatial attention.MEFF module,as a multi-level enhanced feature connection branch,introduces deformable convolution to enhance the information fusion between feature graphs.Experimental results show that the proposed method has good performance on common text data sets.Among them,the comprehensive index F of ICDAR2015 and Total-Text data sets reached 88.1%and 86.5%,respectively,which increased by 0.8%and 1.8%compared with the original method.
Keywords：natural scene text detectionattention mechanismpixel point classificationdilated convolutionfeature fusion
Publication Date：2024-05-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：13( 1-13 )