Existing scene text recognizers are prone to be troubled by blurred text images, leading to poor performance in practical applications. Therefore, several scene text image super-resolution models have been proposed as the pre-processor for text recognizers to improve the quality of input images. However, real-world training samples for the scene text image super-resolution task are difficult to collect. In addition, existing STISR models only learn to transform low-resolution (LR) text images into high-resolution (HR) text images while ignoring blurring patterns from HR to LR images. This study proposes a blurring pattern aware module (BPAM), which learns blurring patterns from existing real-world HR-LR pairs and transfers them to other HR images for generating LR images with different degrees of degradation. Therefore, the proposed BPAM can produce massive HR-LR pairs for STISR models to compensate for the deficiency of training data, significantly improving performance. The experimental results show that when equipped with the proposed BPAM, the performance of SOTA STISR methods can be further improved. For instance, the SOTA method TG achieves a 5.8% improvement in recognition accuracy with CRNN for evaluation.