文献类型：期刊文献

英文题名：MTMFNet: multi-threshold and multi-scale feature fusion network for text detection

作者：Dai, Lei[1];Gao, Wen[1];Tang, Chengyu[1];Wang, Min[1];Chen, Zhihua[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：41

期号：10

起止页码：7233

外文期刊名：VISUAL COMPUTER

收录：;EI(收录号：20250717852626);WOS:【SCI-EXPANDED(收录号:WOS:001409469900001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant Nos. 62306113 and 62272164).

语种：英文

外文关键词：Scene text detection; Attention mechanism; Segmentation threshold; Multi-scale feature fusion

摘要：Image feature information is unable to be fully exploited by most existing scene text detection methods, resulting in multi-scale text error detection and crooked text missing detection. This paper focuses on small text detection missing, large text false detection, and low scene text boundary location accuracy caused by multi-scale scene text changes. To overcome these limitations, this paper proposes a text detection method based on multi-threshold and multi-scale feature fusion (MTMFNet). The lightweight network ResNet18 serves as the primary backbone network. Deformable convolution kernels are utilized in feature pyramids to expand the receptive field. Fused attention-oriented mechanism module is introduced to utilize various feature information efficiently and improve the ability of detail feature extraction. Multi-threshold boundary detection module is deployed to generate more accurate text boundaries. This multi-branch structure-based module can sense the cooperative relationship of text boxes using differentiable binarization submodules with associative thresholds. Experimental results indicate that MTMFNet obtains the best comprehensive performance compared with most state-of-the-art text detection methods in terms of four evaluation metrics on three datasets involving the multi-directional text, curved text, and multi-scale text. The related code of our method is available at https://github.com/Jinfu/MiLNet.