In Python's ‘Pytesseract’ -Class Library use tutorial

In Python's ‘Pytesseract’ -Class Library use tutorial If you are looking for a Python class library for optical character recognition (OCR), then 'Pytesseract' is a powerful choice.‘Pytesseract’ is a Python packaging library based on the Tesseract OCR engine, which can help you identify text information in the image.This tutorial will show you how to use the "Pytesseract" class library to identify Chinese text, and explain the complete programming code and related configuration when necessary. 1. Install the Tesseract OCR engine and the "Pytesseract" library First, you need to install the TESSERACT OCR engine.This can be completed in the terminal through the following command (only Linux and Mac systems): sudo apt-get install tesseract-ocr Or (only MAC system): brew install tesseract For Windows system, visit the official website of TESSSERACT OCR (https://github.com/tesseract-cr/tesseract) download for Windows installation program, and select to the system environment variables during the installation process. After the installation is complete, you can use the PIP command to install the "Pytesseract" library: pip install pytesseract After the installation is completed, you can start using the "Pytesseract" class library for Chinese text to identify. 2. Import the necessary library Before writing and identifying the script, we first need to import the necessary libraries.The following are examples of importing 'pytesseract' and other necessary class libraries: python import pytesseract from PIL import Image Here, we have introduced the "Pytesseract" class library and Python Imaging Library (Pil) to read and process pictures. 3. Identify Chinese text The next step is to open the image to be recognized and read the text.The following is an example code that uses the "Pytesseract" class library to identify Chinese text: python # 打 image = Image.open('image.jpg') # Use Pytesseract for text recognition result = pytesseract.image_to_string(image, lang='chi_sim') # Output recognition text print(result) Here, we first use the Image module in the Pil library to open the picture to be recognized (please make sure that the image file to be recognized is 'Image.jpg' before running the script and put it in the directory where the script is located). Then, we use the 'Image_to_String' method of the ‘Pytesseract’ -IMAGE_TO_STRING '.The second parameter 'LANG' is used to specify the language (in this case is simplified Chinese). Finally, we printed identified text results. 4. Configure Tesseract OCR engine (optional) In some cases, you may need to configure some configuration of the Tesseract OCR engine to improve the accuracy of identification.For this reason, you can use the 'Image_to_String' method of the "Pytesseract" class library to provide additional parameters.The following is an example, showing how to specify PSM (Page Segmentation Mode) and OEM (OCR Engine Mode) parameters: python result = pytesseract.image_to_string(image, lang='chi_sim', config='--psm 6 --oem 1') Here, we use the '-psm 6' parameter to segment the page according to a single block in the image.'-OEM 1' parameter is used to select the OCR engine mode. You can choose and adjust these parameters as needed to get the best identification results. Summarize: Through this tutorial, you learn how to use the "Pytesseract" library in Python for Chinese text recognition.You first installed the Tesseract OCR engine and the "Pytesseract" library and imported the necessary libraries.Then, you learn how to open the image and use the 'Image_to_String' method of the "Pytesseract" class library to identify the image by text.Finally, you also understand how to use additional configuration parameters to adjust the TESSERACT OCR engine to improve the accuracy of recognition. I hope this tutorial will be helpful to you. May you successfully conduct Chinese text OCR recognition!