To address the challenges of collecting real-world high-resolution (HR) and low-resolution (LR) paired training images and the insufficient utilization of text prior information in existing scene text image super-resolution (STISR) methods, an improved method named enhanced text prior guided scene text image super-resolution (ETPGSR) was proposed. The text prior generator was upgraded from convolutional recurrent neural networks(CRNN) to DenseNet-RNN. The text prior transformer was improved by expanding the feature map size through a 5-layer transposed convolution to match the dimensions of the feature maps in the super-resolution branch. A fusion module was introduced to combine text prior features with image super-resolution features. Additionally, a scene image degradation simulation method combining learnable neural operators and conventional operators was proposed to construct a HR-LR paired dataset. Experimental results show that with pretraining on simulated dataset and an improved text prior branch network architecture, text recognition accuracy of 64.5%, 60.8%, and 54.0% is achieved using attentional scene text recognizer with flexible rectification(ASTER), multi-object rectified attention network(MORAN), and CRNN, respectively, on the benchmark TextZoom dataset. These results surpassed multiple comparison models such as text prior guided super-resolution (TPGSR) and text attention network(TATT). Furthermore, generalization tests conducted on the ICDAR2015 and SVT datasets also demonstrate superior performance over the aforementioned models. It is concluded that ETPGSR can effectively enhance STISR performance.