我尝试使用OpenCV和Python-tesseract应用OCR将以下图像转换为文本：Original image。
但是tesseract还没有成功地正确读取图像。它读到：uleswylly Bie 7 Srp a7。
我已经采取了以下步骤来预处理图像，然后再将其馈送到tesseract：
1.首先，我放大图像：

# Image scaling
def set_image_dpi(img):
    # Get current dimensions of the image
    height, width = img.shape[:2]

    # Define scale factor
    scale_factor = 6

    # Calculate new dimensions
    new_height = int(height * scale_factor)
    new_width = int(width * scale_factor)

    # Resize image
    return cv2.resize(img, (new_width, new_height))

图像结果：result1.png
1.规格化图像：

# Normalization
norm_img = np.zeros((img.shape[0], img.shape[1]))
img = cv2.normalize(img, norm_img, 0, 255, cv2.NORM_MINMAX)

图像结果：result2.png
1.然后，我删除一些噪音：

# Remove noise
def remove_noise(img):
    return cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 15)

图像结果：result3.png
1.获取灰度图像：

# Get grayscale
def get_grayscale(img):
    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

图像结果：result4.png
1.应用阈值：

# Thresholding
def thresholding(img):
    return cv2.threshold(img, 150, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) [1]

图像结果：result5.png
1.反转图像颜色：

# Invert the image
def invert(img):
    return cv2.bitwise_not(img)

图像结果：result6.png
1.最后，我将图像传递给pytesseract：

# Pass preprocessed image to pytesseract
text = pytesseract.image_to_string(img)
print("Text found: " + text)

pytesseract输出：“uleswylly Bie7 Srp a7”
我想改善我的预处理，使pytesseract可以真正读取图像？任何帮助将不胜感激！
先谢谢你，
斯廷纳特

import cv2 import numpy as np import pytesseract pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' # May be required when using Windows img = cv2.imread('result3.png') # Read result3.png # Add padding with the color of the top left pixel pad_color = img[0, 0, :] padded_img = np.full((img.shape[0]+10, img.shape[1]+10, 3), pad_color, np.uint8) padded_img[5:-5, 5:-5, :] = img cv2.floodFill(padded_img, None, (0, 0), (255, 100, 100), loDiff=(10, 10, 10), upDiff=(10, 10, 10)) # Fill the background with blue color. cv2.imwrite('result7.png', padded_img) # Convert from BGR to HSV color space, and extract the saturation channel. hsv = cv2.cvtColor(padded_img, cv2.COLOR_BGR2HSV) s = hsv[:, :, 1] cv2.imwrite('result8.png', s) # Apply thresholding (use `cv2.THRESH_OTSU` for automatic thresholding) thresh = cv2.threshold(s, 0, 255, cv2.THRESH_OTSU)[1] cv2.imwrite('result9.png', thresh) # Pass preprocessed image to PyTesseract text = pytesseract.image_to_string(thresh, config="--psm 6") print("Text found: " + text)