In real life, the emergence of haze brings great inconvenience and does harm to traffic and pedestrian safety, whereas previous studies paid less attention to text detection in haze scenes. In this experiment, we found that the candidates obtained by the general non-maximum suppression (NMS) method or the soft-NMS method are difficult to precisely match the ground truth, and the incomplete feature extraction will affect the final performance. In this work, a haze scene text detection framework is skillfully designed. An optimized NMS and an optimized long short-term memory for spatial feature extraction and temporal feature extraction are proposed to improve the text detection performance. In addition, a hazing scene text dataset (named HSText-1000) and a hybrid scenario text dataset (named MHSText-4600) have been built in our work for evaluating the performance conveniently, which have been released and can be downloaded from https://github.com/lyy0117/lyy. Experimental results illustrate that our method is superior to some state-of-the-art methods in the hazing scene and the hybrid scene. Meanwhile, we achieved competitive results in nonhaze’s public dataset (ICDAR 2013), which means that our method has satisfactory adaptability. We will release code to facilitate community research.