This paper describes our participation in the QALB-2015 Automatic Correction of Arabic Text shared task. We employed various tools and external resources to build a rule based correction method. Hand written linguistic rules were added by using existing lexicons and regular expressions. We handled specific errors with dedicated rules reserved for nonnative speakers. The system is simple as it does not employ any sophisticated machine learning methods and it does not correct punctuation errors. The system achieved results comparable to other approaches when the punctuation errors are ignored with an F1 of 66.9% for native speakers’ data and an F1 of 31.72% for the non-native speakers’ data.