Install the necessary libraries: Ensure that you have OpenCV and other required libraries installed. You can use pip or conda to install them.
Load the image: Use OpenCV to load the image file. You can use the cv2.imread() function for this purpose.
Preprocess the image: Depending on the specific requirements of the handwritten text, you might need to perform some preprocessing steps such as resizing, grayscale conversion, thresholding, noise removal, etc. These steps can improve the accuracy of the text recognition. OpenCV provides various functions to assist with image preprocessing.
Text detection: Apply a text detection algorithm to locate regions of interest (ROIs) containing text in the image. You can use techniques like contours, edge detection, or machine learning-based methods for text detection.
Text recognition: Once you have the ROIs containing text, you can use Optical Character Recognition (OCR) techniques to recognize the characters and convert them into text. Tesseract is a popular OCR library that can be used with OpenCV. Install the Pytesseract library to integrate Tesseract with Python.
Extract text and save it: Extract the recognized text from the detected ROIs and save it to a text file using Python's file handling capabilities.