In this paper, we propose a novel Enhanced Self-mined Text Guided Super-resolution Network (ESTGN) for single image super-resolution (SISR). Unlike preceding methods, ESTGN autonomously mines task-related text from images and uses it to guide SR for high-frequency detail restoration. The proposed methods include the Self-mined Text Information Extraction Module, Multi-resolution Text-aware Gradient Balance Module, and Masked Text-conditioned Attention Module. Our method can fully leverage self-mined textual semantic information and enhance gradient propagation in text. We validate our method with extensive experiments on the benchmark dataset, where ESTGN significantly outperforms the baseline model and sets a new state-of-the-art. This work opens up a promising avenue for the integration of text information in image SR tasks.