To address the performance degradation of segmentation-based text detection methods in aliasing text scenes， a Single Direction Projected Transformer （SDPT） was proposed for aliasing text detection. Firstly， multi-scale features were extracted and fused by using deep Residual Network （ResNet） and Feature Pyramid Network （FPN）. Then， the feature map was projected into a vector sequence by using horizontal projection and was fed into the Transformer module to model， thereby mining the relationship between the lines of text. Finally， joint optimization was performed using multiple objectives. Extensive experiments were conducted on the synthetic dataset BDD-SynText and the real dataset RealText. The results show that the proposed SDPT achieves optimal effect for text detection with high aliasing level， and improves F1-Score （IoU75） by at least 21. 36 percentage points on BDD-SynText and 18.11 percentage points on RealText compared with the state-of-the-art text detection algorithms such as Progressive Scale Expansion Network （PSENet） under the same backbone network （ResNet50）， verifying the important role of the proposed method for performance improvement in aliasing text detection.