問題描述

我試圖通過首先將其轉(zhuǎn)換為圖像然后使用 OpenCV 來識別 .pdf 文檔中的文本段落.但是我在文本行而不是段落上得到邊界框.如何設(shè)置一些閾值或其他限制來獲取段落而不是行?

這是示例輸入圖像:

這是我為上述示例得到的輸出:

我試圖在中間的段落上設(shè)置一個邊界框.我正在使用

這就是魔法發(fā)生的地方.我們可以假設(shè)一個段落是一段緊密相連的單詞，為了實現(xiàn)這一點，我們將相鄰的單詞進(jìn)行擴(kuò)張

結(jié)果

導(dǎo)入 cv2將 numpy 導(dǎo)入為 np# 加載圖像，灰度，高斯模糊，Otsu的閾值圖像 = cv2.imread('1.png')灰色 = cv2.cvtColor(圖像，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]# 創(chuàng)建矩形結(jié)構(gòu)元素并擴(kuò)張內(nèi)核 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))dilate = cv2.dilate(閾值，內(nèi)核，迭代=4)# 查找輪廓并繪制矩形cnts = cv2.findContours(擴(kuò)張，cv2.RETR_EXTERNAL，cv2.CHAIN_APPROX_SIMPLE)cnts = cnts[0] 如果 len(cnts) == 2 否則 cnts[1]對于 cnts 中的 c:x,y,w,h = cv2.boundingRect(c)cv2.rectangle(圖像, (x, y), (x + w, y + h), (36,255,12), 2)cv2.imshow('thresh', thresh)cv2.imshow('擴(kuò)張'，擴(kuò)張)cv2.imshow('圖像', 圖像)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()

這篇關(guān)于如何在 Python OpenCV 中檢測文本文檔圖像中的段落是否存在不一致的文本結(jié)構(gòu)的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網(wǎng)！