python - 删除水平下划线

Question

我试图从数百个包含死刑记录信息的 JPG 中提取文本； JPG 由德克萨斯州刑事司法部 (TDCJ) 主持。以下是删除了个人身份信息的示例代码段。

我已经确定下划线是正确 OCR 的障碍--如果我进入，截取一个子片段并手动涂白线，通过 pytesseract 生成 OCR非常好。但是有下划线，它非常糟糕。

如何才能最好地去除这些水平线？我尝试过的:

• 从 OpenCV 文档的演练开始:Extract horizontal and vertical lines by using morphological operations .很快就卡住了，因为我对 C++ 的了解为零。
• 关注 Removing Horizontal Lines in image - 以难以辨认的字符串结尾。
• 关注 Removing long horizontal/vertical lines from edge image using OpenCV - 无法在此处了解调整零数组大小的直觉。

用 c++ 标记这个问题希望有人能帮忙翻译docs walkthrough的第5步到 Python。我已经尝试了一批转换，例如 Hugh Line Transform，但我在图书馆和我之前经验为零的区域中感到黑暗。

import cv2

# Inverted grayscale
img = cv2.imread('rsnippet.jpg', cv2.IMREAD_GRAYSCALE)
img = cv2.bitwise_not(img)

# Transform inverted grayscale to binary
th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
                            cv2.THRESH_BINARY, 15, -2)

# An alternative; Not sure if `th` or `th2` is optimal here
th2 = cv2.threshold(img, 170, 255, cv2.THRESH_BINARY)[1]

# Create corresponding structure element for horizontal lines.
# Start by cloning th/th2.
horiz = th.copy()
r, c = horiz.shape

# Lost after here - not understanding intuition behind sizing/partitioning

Answer 1

到目前为止，所有答案似乎都在使用形态学运算。这里有点不同。如果线条是水平的，这应该会产生相当好的结果。

为此，我使用了您的示例图片的一部分，如下所示。

加载图像，将其转换为灰度并反转。

import cv2
import numpy as np
import matplotlib.pyplot as plt

im = cv2.imread('sample.jpg')
gray = 255 - cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

反转灰度图:

如果您扫描此倒置图像中的一行，您会发现其轮廓看起来不同，具体取决于是否存在线条。

plt.figure(1)
plt.plot(gray[18, :] > 16, 'g-')
plt.axis([0, gray.shape[1], 0, 1.1])
plt.figure(2)
plt.plot(gray[36, :] > 16, 'r-')
plt.axis([0, gray.shape[1], 0, 1.1])

绿色是没有下划线的行，红色是有下划线的行。如果您对每个配置文件取平均值，您会发现红色配置文件的平均值更高。

因此，使用这种方法，您可以检测下划线并将其删除。

for row in range(gray.shape[0]):
    avg = np.average(gray[row, :] > 16)
    if avg > 0.9:
        cv2.line(im, (0, row), (gray.shape[1]-1, row), (0, 0, 255))
        cv2.line(gray, (0, row), (gray.shape[1]-1, row), (0, 0, 0), 1)

cv2.imshow("gray", 255 - gray)
cv2.imshow("im", im)

以下是检测到的红色下划线和清理后的图像。

清理后图像的tesseract输出:

Convthed as th(
shot once in the
she stepped fr<
brother-in-lawii
collect on life in
applied for man
to the scheme i|

使用部分图片的原因现在应该很清楚了。由于原始图像中的个人身份信息已被删除，因此阈值将不起作用。但是当你应用它进行处理时，这应该不是问题。有时您可能需要调整阈值 (16, 0.9)。

删除了部分字母并且仍然保留了一些微弱的线条，结果看起来不太好。如果我能改进它会更新。

更新:

Dis 一些改进；清理并链接字母的缺失部分。我已经评论了代码，所以我相信这个过程很清楚。您还可以检查生成的中间图像以了解它是如何工作的。结果稍微好一点。

清理后图像的tesseract输出:

Convicted as th(
shot once in the
she stepped fr<
brother-in-law. ‘
collect on life ix
applied for man
to the scheme i|

清理后图像的tesseract输出:

)r-hire of 29-year-old .
revolver in the garage ‘
red that the victim‘s h
{2000 to kill her. mum
250.000. Before the kil
If$| 50.000 each on bin
to police.

python 代码:

import cv2
import numpy as np
import matplotlib.pyplot as plt

im = cv2.imread('sample2.jpg')
gray = 255 - cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# prepare a mask using Otsu threshold, then copy from original. this removes some noise
__, bw = cv2.threshold(cv2.dilate(gray, None), 128, 255, cv2.THRESH_BINARY or cv2.THRESH_OTSU)
gray = cv2.bitwise_and(gray, bw)
# make copy of the low-noise underlined image
grayu = gray.copy()
imcpy = im.copy()
# scan each row and remove lines
for row in range(gray.shape[0]):
    avg = np.average(gray[row, :] > 16)
    if avg > 0.9:
        cv2.line(im, (0, row), (gray.shape[1]-1, row), (0, 0, 255))
        cv2.line(gray, (0, row), (gray.shape[1]-1, row), (0, 0, 0), 1)

cont = gray.copy()
graycpy = gray.copy()
# after contour processing, the residual will contain small contours
residual = gray.copy()
# find contours
contours, hierarchy = cv2.findContours(cont, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
for i in range(len(contours)):
    # find the boundingbox of the contour
    x, y, w, h = cv2.boundingRect(contours[i])
    if 10 < h:
        cv2.drawContours(im, contours, i, (0, 255, 0), -1)
        # if boundingbox height is higher than threshold, remove the contour from residual image
        cv2.drawContours(residual, contours, i, (0, 0, 0), -1)
    else:
        cv2.drawContours(im, contours, i, (255, 0, 0), -1)
        # if boundingbox height is less than or equal to threshold, remove the contour gray image
        cv2.drawContours(gray, contours, i, (0, 0, 0), -1)

# now the residual only contains small contours. open it to remove thin lines
st = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
residual = cv2.morphologyEx(residual, cv2.MORPH_OPEN, st, iterations=1)
# prepare a mask for residual components
__, residual = cv2.threshold(residual, 0, 255, cv2.THRESH_BINARY)

cv2.imshow("gray", gray)
cv2.imshow("residual", residual)   

# combine the residuals. we still need to link the residuals
combined = cv2.bitwise_or(cv2.bitwise_and(graycpy, residual), gray)
# link the residuals
st = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 7))
linked = cv2.morphologyEx(combined, cv2.MORPH_CLOSE, st, iterations=1)
cv2.imshow("linked", linked)
# prepare a msak from linked image
__, mask = cv2.threshold(linked, 0, 255, cv2.THRESH_BINARY)
# copy region from low-noise underlined image
clean = 255 - cv2.bitwise_and(grayu, mask)
cv2.imshow("clean", clean)
cv2.imshow("im", im)