Contemporary proven block cryptographic algorithms, like the Advanced Encryption Standard (AES), are used for secure transmission of data in many of the communication systems in use. Classical error detection/correction methods are not suitable for such situations due to the randomness property of encryption. A one bit error in an encrypted message will lead to an average of 50% error rate in the decrypted message. In this paper, error detection and correction is done at the receiver end, without any changes to the encryption algorithm. Errors that are not correctible by existing error correction methods are treated in this paper by looking at their surrounding data. For a specific corpus the space of all possible messages is modeled. The neighborhood of the noise contaminated data is used, along with the space of all possible messages, in order to correct the errors. In case of multiple possible corrections, statistical natural language processing methods are used to rank the possibilities and choose the best one.