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()