Handwritten text acknowledgment is yet an open examination issue in the area of Optical Character Recognition (OCR). This paper prop oses a productive methodology towards the advancement of handwritten text acknowledgment frameworks. The primary goal of this task is to create AI calculation to empower element and information extraction from records with manually written explanations, with an, expect to distinguish transcribed words on a picture. The main aim of this project is to extract text, this text can be handwritten text or it can machine printed text and convert it into computer understandable or wNe can say computer editable form at. To implement thais project we have used PyTesseract which is an open -sourcemOCR engine used to recognize handwritten text and OpenCV a library in python used to solve computer vision problems. So the input image is executed in various steps, first ther e is pre-processing of an image then there is text localization after that there is character segmentation and character recognition and fin ally we have post -processing of image. Further image processingalgorithms can also be used to deal with the multiple characters input in a single image, tilt image, or rotated image. The prepared framework gives a normal precision of more than 95 % with the concealed test picture.