Recovering High-Resolution (HR) images from Low-Resolution (LR) text images is extremely challenging due to factors such as complex backgrounds,blurring,warping and distortion. Existing methods,which predominantly rely on recurrent neural networks to extract textual context,often struggle with capturing long-distance dependencies and leveraging semantic information effectively. To address the above problems,this paper proposes a novel text image super-resolution approach that integrates scene multimodal priors and sparse attention. First,we innovatively introduce a Scene Multimodal Prior Branch (SMPB),which leverages an advanced context parsing unit and a contour perception unit to fully explore and utilize both textual and visual information. Second,a Sparse Attention-Based Super-Resolution (SABSR) enhancement module is designed to extract contextual information from text lines. By utilizing the global receptive field of the multi-head attention mechanism,it effectively constructs inter-character correlations,thereby alleviating the performance degradation commonly encountered with long text sequences. Finally,the model's capability in extracting text contours and processing deformed text is significantly enhanced by a joint loss function that combines gradient contour awareness with text structure perception. Experimental results show that compared to the baseline model Text ATTention network(TATT),our method improves recognition accuracy on the TextZoom test set by 4.3 percentage points on average. Furthermore,it achieves average PSNR and SSIM values of 21.4 dB and 0.790 9,respectively,demonstrating superior performance for text image super-resolution in real-world scenarios.