Common problems and solutions to the python'pytesseract "class library

Common problems and solutions to the python'pytesseract "class library Abstract: Python's Pytesseract class library is a package based on the Tesseract OCR engine to identify text in the picture.However, some common problems may be encountered in the process of using Pytesseract.This article will introduce some common problems and give the corresponding solution to help developers better use the Pytesseract library. 1. Pytesseract When identifying Chinese text, there is a garbled problem Problem description: When using pytesseract in Chinese text recognition, the return results have garbled problems and cannot be displayed correctly. Solution: 1. Determine the Tesseract language package that is correctly installed: Pytesseract to identify text based on Tesseract language package.Before running, make sure that the Chinese language package has been properly installed.Use the following command to install the language package: sudo apt-get install tesseract-ocr-chi-sim 2. Specifying language parameters: When calling Pytesseract for identification, you can identify Chinese by specifying the `Lang` parameter.For example, if you want to identify simplified Chinese, you can use the following code: python import pytesseract text = pytesseract.image_to_string(image, lang='chi_sim') 3. Modify environment variables: In some cases, due to the problem of environmental variable configuration, Pytesseract cannot correctly load the Chinese character set, which leads to garbled problems.The method of solving this problem is to manually specify the path of the testract in the code, as shown below: python import pytesseract pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract' text = pytesseract.image_to_string(image) Make sure to set the correct TESSERACT path. 2. Pytesseract cannot recognize the text in the picture Problem description: When using pytesseract for text recognition, the text in the picture cannot be successfully identified. Solution: 1. Image pre -processing: Pytesseract has higher requirements for the quality of the picture. You can try some pre -processing operations on the picture, such as gray, dual -value, noise, etc.Here are some pre -processing methods that may help improve the accuracy of identification: python import cv2 import pytesseract image = cv2.imread('image.png') # Grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Binarization ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # 去 去 blur = cv2.GaussianBlur(binary, (3, 3), 0) text = pytesseract.image_to_string(blur, lang='eng') 2. Adjust the recognition parameter: Pytesseract provides some adjustable recognition parameters, which can be tried according to the actual situation, such as changing the identification mode, specified PSM parameters, etc.For example, the following code specifies the identification mode as a single character: python import pytesseract text = pytesseract.image_to_string(image, config='--psm 10') You can adjust the value of the `-psm` parameter to get better identification results. In summary, through the above solutions, some common problems encountered when using the Pytesseract library.If you still cannot solve the problem, you can refer to the official documentation of Pytesseract or seek help in related development communities.It is hoped that this article can help developers better use Pytesseract for Chinese text recognition. The complete programming code and related configuration are as follows: python import cv2 import pytesseract # OK TESSERACT path pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract' # Load the picture image = cv2.imread('image.png') # 图片 Pre -processing gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) blur = cv2.GaussianBlur(binary, (3, 3), 0) # 文 文 text = pytesseract.image_to_string(blur, lang='chi_sim') print(text) Please modify the picture path and the TESSERACT path according to the actual situation.If necessary, you can try different pre -processing methods and configuration parameters to improve the accuracy of recognition.