Authors: Chia-Cheng Chang

Description: Detecting small scene text instances in the wild is particularly challenging, where the
influence of irregular positions and nonideal lighting often leads to detection errors. We present
MixNet, a hybrid architecture that combines the strengths of CNNs and Transformers, capable
of accurately detecting small text from challenging natural scenes, regardless of the orientations,
styles, and lighting conditions. MixNet incorporates two key modules: (1) the Feature Shuffle
Network (FSNet) to serve as the backbone and (2) the Central Transformer Block (CTBlock)
to exploit the 1D manifold constraint of the scene text. We first introduce a novel feature
shuffling strategy in FSNet to facilitate the exchange of features across multiple scales,
generating high-resolution features superior to popular ResNet and HRNet. The FSNet
backbone has achieved significant improvements over many existing text detection methods,
including PAN, DB, and FAST. Then we design a complementary CTBlock to leverage central
line based features similar to the medial axis of text regions and show that it can outperform
contour-based approaches in challenging cases when small scene texts appear closely.
Extensive experimental results show that MixNet, which mixes FSNet with CTBlock, achieves
state-of-the-art results on multiple scene text detection datasets