速算作业智能批改 体验

import cv2
import pytesseract

def recognize_and_grade(image_path):
    # Load the image
    img = cv2.imread(image_path)
    
    # Preprocess the image (e.g., grayscale, blur, threshold)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    _, thresholded = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY)
    
    # Use OCR to extract text
    text = pytesseract.image_to_string(thresholded)
    
    # Here you would add logic to parse 'text' and grade the answers
    # For simplicity, let's assume we're just checking if a specific answer is present
    expected_answer = "42"  # Example expected answer
    if expected_answer in text:
        return "Correct!"
    else:
        return "Incorrect. Please try again."

# Example usage
result = recognize_and_grade("path_to_student_work_image.png")
print(result)