Natural Language Processing's role of spelling correction is crucial. Search engines, sentiment analysis, text summarization, and other processes all use it. We strive to find and fix spelling issues in spelling correction, as the name implies. The correction of spelling errors in real-world NLP tasks helps models perform better because we frequently deal with data that contains typos. For users, unclear or inconsistent communication caused by misspelled words can be confusing. The automatic spelling suggestion provided by spell check improves readability and ensures communication clarity. Many packages are useful for spelling correction. Here, we have utilized TextBlob, Levenshtein and spellchecker packages for spelling correction and tested using input text.