Computer automation correction of article errors in English grammar is been studied. First we define the categories of article errors, and missing articles is also included, by using a maximum entropy model, extracting features covering context, part of speech, noun phrase structure and so on, training the model on the training corpus, then use the model to predict and correct the article errors of an input sentence. In the experiment on NUCLE corpus, effects of corpus preprocess, model types and the size of the training corpus are discussed. We make a comparison with the popular Naive Bayes model, at last we introduce the characters of English grammar to improve the model, a F-score of 35.48% is achieved, the result is slightly better than the best result in CoNLL 2013 shared task.