The robustness of text classifier based on the deep neural networks can be improved by correcting adversarial spelling mistakes. The main challenge brought by these mistakes is that it is difficult for the text classifier to recognize the words in the text correctly. Existing works to deal with these mistakes can be divided into two types: optimization of training data and reconstruction of text classification model. However, these works are not suitable for the text classification model that has been deployed, as retraining or reconstruction is always involved. To address the above problem, we propose a two-step spelling correction model, which consists of a misspelled word detector and a misspelled word correcter, referred to as DE-CO. Specifically, we use a detector to recognize misspelled words in the text, use a corrector to correct the misspelled words, and then feed the correction results into the downstream classifier. In this way, without reconstruction or retraining, the normal recognition of words by the text classifier can be guaranteed. We evaluate DE-CO on adversarial examples generated from the Stanford Sentiment Treebank (SST). The classification accuracy of the downstream classifier is improved in a variety of attack scenarios, which demonstrates that DE-CO improves the robustness of the text classifier.