ASANet:Scene Text Recognition With Alternate Self-Attention

Text recognition in complex scenes is a challenging task in Computer Vision. In this paper, we propose an innovative framework for scene text recognition, ASANet, which features an Alternate Attention Enhancement Encoder and a Masked Dual-modal Decoder. The encoder incorporates a 12-layer Alternating Self-Attention Module (ASAM), consisting of both Channel and Spatial Blocks, which significantly enhance the depth and breadth of feature extraction. The decoder employs a strategy that combines masking and sequence alignment modeling, effectively improving character context relevance and prediction accuracy. Extensive experimental results demonstrate that ASANet achieves state-of-the-art performance across several benchmark datasets, with a notable accuracy of 91.2% on our self-constructed Uyghur text dataset, highlighting its superior performance.