AutoCorrect Keyboard with python
Under the guidance of Prof. Kaspar.
Abstract
The project aims to develop an auto correction system for keyboard typing errors using Python and machine learning techniques. The system analyzes the text input in real-time and applies machine learning algorithms to predict and correct typing errors, thereby improving the accuracy and efficiency of the typing process. The project involves training a machine learning model using a large corpus of text data to learn the patterns and rules of language usage. The trained model is then integrated into the keyboard interface to provide automatic correction suggestions to the user. The results of the project demonstrate the potential of machine learning in enhancing the functionality and usability of keyboard interfaces. The project involves training a machine learning model using a large corpus of text data and applying NLP techniques such as tokenization, stemming, and part-of-speech tagging to preprocess the text data. The trained model is then used to identify and correct typing errors by comparing the input text with the learned patterns of language.
Keywords
Introduction
An autocorrect keyboard is a software tool that automatically corrects misspelled words or typos as the user types on a keyboard. Python is a popular programming language that can be used to create an autocorrect keyboard. One of the features that use Natural Language Processing (NLP) is the Autocorrect function. It is specially programmed to generalize all the correct words in the dictionary and looks for the words that are the most comparable to those words not in the vocabulary.
Methodology
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of Artificial Intelligence that allows computers to understand and process natural human language. NLP uses a programming language that enables computers to evaluate and interpret large volumes of natural language data. Whether the language is spoken or written, natural language processing uses artificial intelligence to take real-world input, process it, and make sense of it in a way a computer can understand. At some point in processing, the input is converted to code that the computer can understand.