Style text with decorative elements has a strong visual sense, and enriches our daily work, study and life. However, it introduces new challenges to text detection and recognition. In this study, we propose a text destylized framework, that can transform the stylized texts with decorative elements into a type that is easily distinguishable by a detection or recognition model. We arranged and integrate an existing stylistic text data set to train the destylized network. The new destylized data set contains English letters and Chinese characters. The proposed approach enables a framework to handle both Chinese characters and English letters without the need for additional networks. Experiments show that the method is superior to the state-of-the-art style-related models.