如何在 Python OpenCV 中检测文本文档图像中的段落是否存在不一致的文本结构

问题描述

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:

这是我为上述示例得到的输出:

我试图在中间的段落上设置一个边界框.我正在使用

这就是魔法发生的地方.我们可以假设一个段落是一段紧密相连的单词，为了实现这一点，我们将相邻的单词进行扩张

结果

导入 cv2将 numpy 导入为 np# 加载图像，灰度，高斯模糊，Otsu的阈值图像 = cv2.imread('1.png')灰色 = cv2.cvtColor(图像，cv2.COLOR_BGR2GRAY)模糊 = cv2.GaussianBlur(灰色, (7,7), 0)thresh = cv2.threshold(模糊, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]# 创建矩形结构元素并扩张内核 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))dilate = cv2.dilate(阈值，内核，迭代=4)# 查找轮廓并绘制矩形cnts = cv2.findContours(扩张，cv2.RETR_EXTERNAL，cv2.CHAIN_APPROX_SIMPLE)cnts = cnts[0] 如果 len(cnts) == 2 否则 cnts[1]对于 cnts 中的 c:x,y,w,h = cv2.boundingRect(c)cv2.rectangle(图像, (x, y), (x + w, y + h), (36,255,12), 2)cv2.imshow('thresh', thresh)cv2.imshow('扩张'，扩张)cv2.imshow('图像', 图像)cv2.waitKey()

I am trying to identify paragraphs of text in a .pdf document by first converting it into an image then using OpenCV. But I am getting bounding boxes on lines of text instead of paragraphs. How can I set some threshold or some other limit to get paragraphs instead of lines?

Here is the sample input image:

Here is the output I am getting for the above sample:

I am trying to get a single bounding box on the paragraph in the middle. I am using this code.

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)

This is a classic use for dilate. Whenever you want to connect multiple items together, you can dilate them to join adjacent contours into a single contour. Here's a simple approach:

• Convert image to grayscale and Gaussian blur
• Otsu's threshold
• Dilate to connect adjacent words together
• Find contours and draw contours

Otsu's threshold

Here's where the magic happens. We can assume that a paragraph is a section of words that are close together, to achieve this we dilate to connect adjacent words

Result

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()

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