在图像中应用智能阈值的方法

Question

我正在编写一个 OCR 应用程序（用于希伯来文字）。

应用中的第一部分是阈值，

我的原图是这样的：

这是阈值处理后的样子：

如您所见，大部分都很好，但字母上的“皇冠”或“装饰”有时会消失，就像这个词：

变成：

问题是我在原始图像上应用RGB2GRAY后，黑色的皇冠真的不够黑，因此在阈值处理过程中它们变白了，但很容易看出它“应该”是黑色的，问题是我如何告诉算法检测它...

我目前的阈值化代码使用的是otzu+局部阈值化，这是代码：

def apply_threshold(img, is_cropped=False):
    '''
    this function applies a threshold on the image, 
    the first is Otsu TH on all the image, and afterwards an adaptive TH,
    based on the size of the image. 
    I apply a logical OR between all the THs, becasue my assumption is that a letter will always be black,
    while the background can sometimes be black and sometimes white -
    thus I need to apply OR to have the background white.
    '''
    if len(np.unique(img)) == 2:  # img is already binary
        # return img
        gray_img = rgb2gray(img)
        _, binary_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        return binary_img
    gray_img = rgb2gray(img)
    _, binary_img = cv2.threshold(gray_img.astype('uint8'), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    connectivity = 8
    output_stats = cv2.connectedComponentsWithStats(binary_img.max() - binary_img, connectivity, cv2.CV_32S)
    df = pd.DataFrame(output_stats[2], columns=['left', 'top', 'width', 'height', 'area'])[1:]
    if df['area'].max() / df['area'].sum() > 0.1 and is_cropped and False:
        binary_copy = gray_img.copy()
        gray_img_max = gray_img[np.where(output_stats[1] == df['area'].argmax())]
        TH1, _ = cv2.threshold(gray_img_max.astype('uint8'), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        # curr_img = binary_copy[np.where(output_stats[1] == df['area'].argmax())]
        binary_copy[np.where((output_stats[1] == df['area'].argmax()) & (gray_img > TH1))] = 255
        binary_copy[np.where((output_stats[1] == df['area'].argmax()) & (gray_img <= TH1))] = 0

        gray_img_not_max = gray_img[np.where(output_stats[1] != df['area'].argmax())]
        TH2, _ = cv2.threshold(gray_img_not_max.astype('uint8'), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        binary_copy[np.where((output_stats[1] != df['area'].argmax()) & (gray_img > TH2))] = 255
        binary_copy[np.where((output_stats[1] != df['area'].argmax()) & (gray_img <= TH2))] = 0
        binary_img = binary_copy.copy()
    # N = [3, 5, 7, 9, 11, 13,27, 45]  # sizes to divide the image shape in
    # N = [20,85]
    N = [3, 5, 25]
    min_dim = min(binary_img.shape)
    for n in N:
        block_size = int(min_dim / n)
        if block_size % 2 == 0:
            block_size += 1  # block_size needs to be odd
        binary_img = binary_img | cv2.adaptiveThreshold(gray_img.astype('uint8'), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                                        cv2.THRESH_BINARY, block_size, 10)


    return binary_img

如有任何创意，我们将不胜感激！

Answer 1

一种方法是 Python/OpenCV 中的除法归一化。

输入：

import cv2
import numpy as np

# load image
img = cv2.imread("hebrew_text.jpg")

# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# blur
blur = cv2.GaussianBlur(gray, (99,99), 0)

# divide
divide = cv2.divide(gray, blur, scale=255)

# write result to disk
cv2.imwrite("hebrew_text_division.png", divide)

# display it
#cv2.imshow("thresh", thresh)
cv2.imshow("gray", gray)
cv2.imshow("divide", divide)
cv2.waitKey(0)
cv2.destroyAllWindows()

结果：

完成此操作后，您可能需要阈值，然后通过获取轮廓并丢弃面积小于最小重音标记大小的任何轮廓来清理它。

我还建议，如果可能的话，将图像保存为 PNG 而不是 JPG。 JPG 具有有损压缩并引入了颜色变化。这可能是您在背景中遇到无关标记的一些问题的根源。

在图像中应用智能阈值的方法

Way to apply smart thresholding in images

python

opencv

image-processing

ocr

image-thresholding