Imports: Include necessary libraries for image processing (OpenCV), OCR (Tesseract), data manipulation (Pandas), regular expressions (re), and file handling (os). Configuration: Set the path to your Tesseract OCR installation. Specify the input folder for the images and the output folder for the updated CSV. Adjust the confidence_threshold if needed based on image quality. extract_data_from_image(image_path) Function: Reads an image from the given path using OpenCV (cv2.imread). Performs OCR using Tesseract (pytesseract.image_to_string) to extract text from the image. parse_extracted_text(text) Function: Uses regular expressions to search for patterns within the extracted text to find: Name (e.g., "Name: John Doe") Date Seen (e.g., "Date Seen: 08/15/2023") Total amount (e.g., "Total: $150.00") Check Number (e.g., "Check Number: 12345") If all fields are found, returns a dictionary with the extracted data; otherwise, returns None to indicate parsing failure. Main Script: Load Excel Data: Loads the Excel spreadsheet using Pandas (pd.read_excel). Process Images and Match Data: Loops through each file in the input folder. Checks if the file is an image (supported formats: .jpg, .png, .jpeg). Extracts text from the image using extract_data_from_image(). Parses the extracted text using parse_extracted_text(). If parsing is successful: Attempts to find a matching row in the DataFrame based on the extracted name and date. If a match is found: Compares the extracted total to the 'total sum paid' in the Excel sheet. If totals match: Updates the 'check number' column in the DataFrame with the extracted check number. If totals don't match: Adds a "disc__" prefix along with the check number to the 'check number' column. If no match is found: Prints an error message indicating the missing data. If parsing fails: Prints an error message. Save Updated Excel: Saves the updated DataFrame back to a new Excel file (updated_data.xlsx) in the specified output folder.