Use Pytesseract to implement automatic recognition of the picture verification code

Use Pytesseract to implement automatic recognition of the picture verification code With the development of network applications, the verification code has gradually become one of the important means to prevent malicious crawlers and automated attacks.However, for users, entering complex verification code verification texts may be very tedious and time -consuming.Therefore, automatic recognition verification code has become one of the hot issues that many developers are concerned about. In Python, we can use the Pytesseract library to achieve automatic recognition of the picture verification code.Pytesseract is a packaging library of a Tesseract OCR engine that can be recognized by text.The following is a simple implementation example: First, we need to install the necessary library.Run the following command in the terminal: pip install pytesseract pip install pillow Next, we will download a picture with a text verification code from the Internet (for example, the link of the verification code picture is https://example.com/captcha.png). Then we need to use Python to write the following code: python import pytesseract from PIL import Image import requests # Download verification code picture from the Internet captcha_url = "https://example.com/captcha.png" response = requests.get(captcha_url) captcha_image = Image.open(BytesIO(response.content)) # Pre -processing the verification code picture captcha_image = Captcha_image.convert ("L") # Captcha_image = Captcha_image.point (lambda x: 0 if x <127 ELSE 255, "1") # # Use Pytesseract for text recognition captcha_text = pytesseract.image_to_string(captcha_image, lang="eng") Print ("recognition results:" + Captcha_text) In the above code, we first introduced the necessary library, and then downloaded the verification code picture from the network using the `requests.get` method, and opened it with the` Image.open` method.Next, we have made some pre -processing of the verification code picture, including converting the image into gray images and performing dual -value processing. Finally, we use the `pytesseract.image_to_string` method to pass the pre -processed picture to Pytesseract for text recognition.`Lang =" ENG "` Parameters indicate to be identified in English language. If you want to process the Chinese verification code, you can replace it to `lang =" chi_sim ". Finally, we show the identification results by printing output. It should be noted that Pytesseraact depends on the Tesseract OCR engine, so before use, you need to ensure that the Tesseract OCR is properly installed and configured its path to the system environment variables.In the Windows system, you can access https://github.com/ub-mannheim/tesseract/wiki to download and install the latest version of Tesseract OCR.After the installation is complete, add the installation path of the Tesseract OCR to the system environment variable to ensure that the Pytesseract can run properly. The automatic recognition of the use of PytesSseract for the verification code can greatly simplify the user's operating process and improve the user experience.However, it should be noted that due to the diversity of the verification code, completely accurate identification is not always feasible.Therefore, in practical applications, we also need to verify the effectiveness of the results of the identification.