Sentence Correction using RNN is simple problem in which we provide text data in corrupted form(gramatical mistake,short forms of some words like'ppl' for'people')to the input and output we get is the correct uncorrupted form of that text data.It can be used as a preprocessing step in a language transaltion model where the input language(in corrupted form) can be converted to uncorrupted form and then pass to a model to output the translated text and thus can help in increasing the efficiency of the language translation model.This case study will be useful for increasing the efficiency as many NLP tasks,since any model will learn from uncorrupted correct text and will be able to predict correctly the target task.Moreover it would be useful in text messaging apps where we could enter a corrupted text and it would suggest us the correct uncorrupted text before sending the text to anyone. Since the task at hand comprises of textual data,in which one form of corrupted textual data is to be converted into uncorrupted form while preserving the semantic meaning of the text.The task is similar to a language translation.The task can be converted to DL problem using LSTM's,GRU's and RNN.Since these archtectures help us to take in account the semantic meaning of text and we can use encoder decoder model to encode corrupted text and then decode it to uncorrupted form. We used to different datasets for our task.one The other dataset we got from https://www.cl.cam.ac.uk/research/nl/bea2019st/:fce_v2.1.It contains sentences with spelling and gramatical mistakes.We used subset of 5k datapoints only since we do not want our model to get biased towards one type of dataset.The dataset is in m2 format. Latency: As far as the latecy is concerned.Our model should output quickly(can take seconds) if it is used as preprocessing step for any other NLP tasks.But if it is used in a text messaging apps the output should be quick within milliseconds.