Advanced text processing skills and technology in Pytesseract library

Pytesseract is a Python library for Optical Character Recognition (OCR), which can be used to extract text from the image.In addition to the basic image text extraction function, Pytesseract also provides some advanced text processing skills and techniques, making text extraction more accurate and reliable. Here are some advanced text processing skills and techniques in Pytesseract library: 1. Language settings: Pytesseract supports text recognition of multiple languages.Before use, you can specify the path of the Tesseract OCR engine by setting the `pytesseract.pytesseract.tesseract_cmd` property.In addition, you can also use the `pytesseract.pytesseract.get_languages ()` function to obtain the supported language list. 2. Image pre -processing: In order to improve the accuracy of identification, some pre -processing operations can be performed on the input image.Common pre -processing operations include ash conversion, dual -value, removal of noise, etc.Can be pre -processed using libraries such as Pillow or OpenCV. 3. Regional recognition: If only a specific area contains text in the image, the accuracy of recognition can be improved by selecting the area by specified boxes.You can use the function to obtain the coordinates of the text area detected in the image in the image. 4. Text configuration parameter: You can use the `pytesseract.image_to_string () function to specify some configuration options of the TESSERACT engine.For example, you can tell the engine by setting `config = '-psm 3'` to identify the text using the automatic page segmentation mode. 5. Multi -page documentation: Pytesseract also supports identification of texts on multiple pages.You can stitch multiple images into a multi -page document, and then use the `pytesseract.image_to_pdf_or_hocr () function to extract the text.This function can return the PDF or HOCR file format of the result text. The following is an example code that shows how to use the Pytesseract library for simple text extraction operations: python import pytesseract from PIL import Image # Set Tesseract OCR engine path pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' # Load the image image = Image.open('image.jpg') # Icon pre -processing (grayscale conversion and dual value) image = image.convert('L') image = image.point(lambda x: 0 if x < 128 else 255, '1') # Extract text text = pytesseract.image_to_string(image, lang='chi_sim') # Print results print(text) The above code converts the image to a gray image, and the two -value processing is performed, and then the text is extracted using the `IMAGE_TO_STRING ()` function.In this example, we use Chinese simplified language packs (`Lang = 'Chi_sim'`) to ensure that Chinese text can be extracted correctly. It should be noted that in order to run the Pytesseract normally, you need to install the Tesseract OCR engine on the computer, and set the `Tesseract_cmd` property in the code to the correct path of the engine. In summary, Pytesseract provides rich advanced text processing skills and techniques. It can improve the accuracy and reliability of text extraction through appropriate configuration and pre -processing operations.