Handwritten text recognition and correction is the process of converting handwritten text into digital form and correcting errors within the recognized text. Natural language processing tools, computer vision methods, and machine learning algorithms are combined to achieve this. The recognition process involves extracting features, and classifying characters or words. The correction process uses a language model to identify errors and suggest corrections based on context and grammar rules. Handwritten text recognition and correction has numerous applications in fields such as document digitization, signature verification, and historical document analysis. However, it remains a challenging task due to the variability of handwriting styles and the complexity of human language.