Image process processing instance tutorial used by Pytesseract and OpenCV

Image process processing instance tutorial used by Pytesseract and OpenCV Pytesseract is an OCR (optical character recognition) library that can convert text in the image to readable text format.OpenCV is a widely used computer vision library that provides various image processing and analysis functions.Based on the use of these two libraries, strong image processing can be performed. This tutorial will introduce how to use Pytesseract and OpenCV to process images and extract text information from it.Before the beginning, we need to ensure that Pytesseract and OpenCV have been installed correctly and set up related configurations. First, we need to install the Pytesseract library.You can use the following command to install: pip install pytesseract Next, we need to install the OpenCV library.You can use the following command to install: pip install opencv-python After the installation is complete, we also need to download the Tesseract OCR engine.The download address is: https://github.com/tesseract-cr/tesseract/wiki.Select the correct version of the TESSERACT according to your operating system and install it according to the instructions. Code example: python import cv2 import pytesseract # Read the image image = cv2.imread('image.png') # Converted to gray image gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Use pytesseract to extract text text = pytesseract.image_to_string(gray, lang='chi_sim') # Printing and extraction text print(text) In this example, we first read a image file with the `cv2.imread` function.We then converted the image to a gray image, which is the requirement for Pytesseract to identify the text.Next, we use the `pytesseract.image_to_string` function to extract the text in the image and store the extracted text in the variable` text`.Finally, we print the extracted text with the `Print` statement. In this example, we assume that the image file is called `Image.png`.You can modify the file name according to your needs. Make sure that the image file is placed in the same directory as the code file before running the code, or the path of the image file is modified as needed. It should be noted that accurately identifying texts may need to prepare some images, such as de -noise, two -value, etc.You can use the various image processing functions of OpenCV to complete these tasks to improve the accuracy of recognition. By combining Pytesseract and OpenCV, we can easily process images and extract the text information.This combination is very useful in many scenarios, such as automated image processing, information extraction, etc. I hope this tutorial will help you!