We present a method for detecting and correcting multiple real-word spelling er- rors using the Google Web 1T 3-gram data set and a normalized and modified ver- sion of the Longest Common Subsequence (LCS) string matching algorithm. Our method is focused mainly on how to im- prove the detection recall (the fraction of errors correctly detected) and the correc- tion recall (the fraction of errors correctly amended), while keeping the respective precisions (the fraction of detections or amendments that are correct) as high as possible. Evaluation results on a standard data set show that our method outperforms two other methods on the same task.

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