  Spell correction is used to detect and correct orthographic mistakes in
texts. Most of the time, traditional dictionary lookup with string similarity
methods is suitable for the languages that have a less complex structure such
as the English language. However, the Azerbaijani language has a more complex
structure and due to its morphological structure, the derivation of words is
plenty that several words are derived from adding suffices, affixes to the
words. Therefore, in this paper sequence to sequence model with an attention
mechanism is used to develop spelling correction for Azerbaijani. Total 12000
wrong and correct sentence pairs used for training, and the model is tested on
1000 real-world misspelled words and F1-score results are 75% for distance 0,
90% for distance 1, and 96% for distance 2.
