从图像中提取矩形内的文本

使用Pytesseract提取文本时，对图像进行预处理非常重要。通常，我们要对文本进行预处理，以使要提取的文本为黑色，背景为白色。为此，我们可以使用Otsu的阈值获取二进制图像，然后执行形态学运算以过滤和去除噪声。这是一条管道：

• 将图像转换为灰度并调整大小
• 大津的二进制图像阈值
• 反转图像并执行形态学操作
• 查找轮廓
• 使用轮廓近似，长宽比和轮廓区域进行过滤
• 消除不必要的噪音
• 执行文字识别

转换为灰度后，我们使用imutils.resize()Otsu的二值图像阈值调整图像大小。现在，图像只有黑色或白色，但是仍然有不必要的噪点

From here we invert the image and perform morphological operations with a horizontal kernel. This step merges the text into a single contour where we can filter and remove the unwanted lines and small blobs

Now we find contours and filter using a combination of contour approximation, aspect ratio, and contour area to isolate the unwanted sections. The removed noise is highlighted in green

Now that the noise is removed, we invert the image again to have the desired text in black then perform text extraction. I've also noticed that adding in a slight blur enhances recognition. Here's the cleaned image we perform text extraction on

We give Pytesseract the --psm 6 configuration since we want to treat the image as a uniform block of text. Here's the result from Pytesseract

6745 63 6 10.50
2245 21 18 17
525 4 22 0.18
400 4 a 0.50
300 3 4 0.75
200 2 3 0.22
2575 24 3 0.77
1950 ii 12 133

输出不是完美的，但接近。您可以在此处尝试其他配置设置

import cv2
import pytesseract
import imutils

pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Resize, grayscale, Otsu's threshold
image = cv2.imread('1.png')
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Invert image and perform morphological operations
inverted = 255 - thresh
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15,3))
close = cv2.morphologyEx(inverted, cv2.MORPH_CLOSE, kernel, iterations=1)

# Find contours and filter using aspect ratio and area
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.01 * peri, True)
    x,y,w,h = cv2.boundingRect(approx)
    aspect_ratio = w / float(h)
    if (aspect_ratio >= 2.5 or area < 75):
        cv2.drawContours(thresh, [c], -1, (255,255,255), -1)

# Blur and perform text extraction
thresh = cv2.GaussianBlur(thresh, (3,3), 0)
data = pytesseract.image_to_string(thresh, lang='eng',config='--psm 6')
print(data)

cv2.imshow('close', close)
cv2.imshow('thresh', thresh)
cv2.waitKey()