  Natural scene text detection is a significant challenge in computer vision,
with tremendous potential applications in multilingual, diverse, and complex
text scenarios. We propose a multilingual text detection model to address the
issues of low accuracy and high difficulty in detecting multilingual text in
natural scenes. In response to the challenges posed by multilingual text images
with multiple character sets and various font styles, we introduce the SFM Swin
Transformer feature extraction network to enhance the model's robustness in
detecting characters and fonts across different languages. Dealing with the
considerable variation in text scales and complex arrangements in natural scene
text images, we present the AS-HRFPN feature fusion network by incorporating an
Adaptive Spatial Feature Fusion module and a Spatial Pyramid Pooling module.
The feature fusion network improvements enhance the model's ability to detect
text sizes and orientations. Addressing diverse backgrounds and font variations
in multilingual scene text images is a challenge for existing methods. Limited
local receptive fields hinder detection performance. To overcome this, we
propose a Global Semantic Segmentation Branch, extracting and preserving global
features for more effective text detection, aligning with the need for
comprehensive information. In this study, we collected and built a real-world
multilingual natural scene text image dataset and conducted comprehensive
experiments and analyses. The experimental results demonstrate that the
proposed algorithm achieves an F-measure of 85.02\%, which is 4.71\% higher
than the baseline model. We also conducted extensive cross-dataset validation
on MSRA-TD500, ICDAR2017MLT, and ICDAR2015 datasets to verify the generality of
our approach. The code and dataset can be found at
https://github.com/wangmelon/CEMLT.
