python - 如何在 Python OpenCV 中检测文本文档图像中不一致文本结构的段落

Question

我试图通过首先将其转换为图像然后使用 OpenCV 来识别 .pdf 文档中的文本段落。但是我在文本行而不是段落上得到边界框。如何设置一些阈值或其他一些限制来获取段落而不是行？

这是示例输入图像:

这是我为上述示例获得的输出:

我试图在中间的段落上获得一个边界框。我正在使用 this代码。

import cv2
import numpy as np

large = cv2.imread('sample image.png')
rgb = cv2.pyrDown(large)
small = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)

# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
kernel = np.ones((5, 5), np.uint8)
grad = cv2.morphologyEx(small, cv2.MORPH_GRADIENT, kernel)

_, bw = cv2.threshold(grad, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1))
connected = cv2.morphologyEx(bw, cv2.MORPH_CLOSE, kernel)

# using RETR_EXTERNAL instead of RETR_CCOMP
contours, hierarchy = cv2.findContours(connected.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#For opencv 3+ comment the previous line and uncomment the following line
#_, contours, hierarchy = cv2.findContours(connected.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

mask = np.zeros(bw.shape, dtype=np.uint8)

for idx in range(len(contours)):
    x, y, w, h = cv2.boundingRect(contours[idx])
    mask[y:y+h, x:x+w] = 0
    cv2.drawContours(mask, contours, idx, (255, 255, 255), -1)
    r = float(cv2.countNonZero(mask[y:y+h, x:x+w])) / (w * h)

    if r > 0.45 and w > 8 and h > 8:
        cv2.rectangle(rgb, (x, y), (x+w-1, y+h-1), (0, 255, 0), 2)


cv2.imshow('rects', rgb)
cv2.waitKey(0)

Answer 1

这是 dilate 的典型情况.每当您想将多个项目连接在一起时，您可以扩大它们以将相邻的轮廓连接成一个轮廓。这是一个简单的方法:

1. 获取二进制图像。 Load the image , 转换为 grayscale , Gaussian blur , 然后 Otsu's threshold获取二值图像。

2. 将相邻的词连接在一起。我们创建一个 rectangular kernel和 dilate将各个轮廓合并在一起。

3. 检测段落。我们从这里find contours , 得到矩形 bounding rectangle coordinates和 highlight the rectangular contours .

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Otsu 获取二值图像的阈值

神奇的地方就在这里。我们可以假设一个段落是靠在一起的一段单词，为了实现这一点，我们扩大以连接相邻的单词

结果

import cv2
import numpy as np

# Load image, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (7,7), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Create rectangular structuring element and dilate
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
dilate = cv2.dilate(thresh, kernel, iterations=4)

# Find contours and draw rectangle
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)

cv2.imshow('thresh', thresh)
cv2.imshow('dilate', dilate)
cv2.imshow('image', image)
cv2.waitKey()