OCR阅读的明显不一致的灰色背景

Question

大约2年前，我问了一个问题一个令人满意的答案。认为最近的脚本已经返回了很多错误，超过30％，所以我决定更改方法，然后提出一个更通用的问题，用原始图像进行思考，而不是我在原始问题中使用的处理过的图像。

这是原始内容：

如您所见，这些示例是原始扫描文档的切片。

问题在于它们的质量不一致，无论是原始打印还是次数扫描。有时数字脱颖而出，有时不脱颖而出。有时我有一个较深的灰色，有时更轻。有时我会得到错误的打印，白线显示打印机未能放置墨水。

此外，它们的字体是“紧密”的方式，因为这些数字彼此之间太近，有时甚至触摸，从而使我无法简单地将每个数字分开以清洁和单独。

我已经尝试了使用OpenCV的各种方法，例如各种模糊：

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray,(5,5),0) # Innitial cleaning
s_thresh = cv2.threshold(blurred, 120, 255, cv2.THRESH_BINARY_INV)[1]
o_thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
ac_thres = cv2.adaptiveThreshold(blurred,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV,5,10)
ag_thres = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 5, 4)

还连接的组件：

ret, thresh = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
gray_img = cv2.cvtColor(opening, cv2.COLOR_BGR2GRAY)
_, blackAndWhite = cv2.threshold(gray_img, 127, 255, cv2.THRESH_BINARY_INV)

nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(blackAndWhite, None, None, None, 8, cv2.CV_32S)
sizes = stats[1:, -1]  # get CC_STAT_AREA component
img2 = np.zeros((labels.shape), np.uint8)

for i in range(0, nlabels - 1):
    if sizes[i] >= 4:  
        img2[labels == i + 1] = 255

res = cv2.bitwise_not(img2)
gaussian = cv2.GaussianBlur(res, (3, 3), 0)

unsharp_image = cv2.addWeighted(res, 0.3, gaussian, 0.7, 0, res)

但是我仍然得到最多不一致的结果。

我应该改变我的方法吗？你们会推荐什么？

Answer 1

这是我原始答案的重新审视方法（现在，在python！！）中进行了完全实现。我正在使用k cmyk cmyk 颜色空间以获取二进制图像。二进制图像是通过 otsu阈值 + a bias bial 的Little Bit ，施加最小面积过滤器到Teserract。

我在这里使用几个图书馆。 iMutils用于在目录中读取图像，OS用于连接路径的 pytesseract ocr。让我们看看代码：

# Imports:
import pytesseract  # tesseract (previous installation)
import numpy as np  # numpy
import cv2  # opencv
import os  # os for paths
from imutils import paths

# Image path:
rootDir = "D:"
baseDir = "opencvImages"
subBaseDir = "numbers"

# Otsu bias:
threshBias = 1.2

# Create os-independent path:
path = os.path.join(rootDir, baseDir, subBaseDir)

# Get the test images paths:
imagePaths = sorted(list(paths.list_images(path)))

# Loop over the test images and OCR them:
for imagePath in imagePaths:

    # Load the image via OpenCV:
    currentImage = cv2.imread(imagePath, cv2.IMREAD_COLOR)

    # Show image:
    showImage("Current Image", currentImage)

    # Convert to float and divide by 255:
    imgFloat = currentImage.astype(np.float) / 255.

    # Calculate channel K:
    kChannel = 1 - np.max(imgFloat, axis=2)

    # Convert back to uint 8:
    kChannel = (255 * kChannel).astype(np.uint8)

    # Threshold via Otsu:
    autoThresh, binaryImage = cv2.threshold(kChannel, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    # Add a little bias and threshold again:
    autoThresh = threshBias * autoThresh
    _, binaryImage = cv2.threshold(kChannel, autoThresh, 255, cv2.THRESH_BINARY)
    showImage("Current Image (Binary)", binaryImage)

    # Apply a filter area of minimum 50 pixels:
    minArea = 50
    binaryImage = areaFilter(binaryImage, minArea)
    showImage("Current Image (Filtered)", binaryImage)

    # Invert Image:
    binaryImage = 255 - binaryImage

    # Setting up tesseract:
    pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'  # for Windows
    custom_config = r'--oem 3 --psm 6'
    text = pytesseract.image_to_string(binaryImage, config=custom_config)

    # Show recognized text:
    print("Text is: " + text)

有几个定义的功能。 showimage只是我的自定义功能，可以通过 openCV的高级GUI 在窗口中显示图像。窗口弹出后，按任何键继续评估脚本。 aide filter函数从前是相同的函数。它适用于二进制图像的最小面积过滤器：

# Defines a re-sizable image window:
def showImage(imageName, inputImage):
    cv2.namedWindow(imageName, cv2.WINDOW_NORMAL)
    cv2.imshow(imageName, inputImage)
    cv2.waitKey(0)

# Applies a minimum blob area filter to an input binary image:
def areaFilter(binaryImage, minArea):
    totalComponents, labeledPixels, componentsStats, componentsCentroids = cv2.connectedComponentsWithStats(binaryImage,
                                                                                                            connectivity=4)
    remaining_comp_labels = [i for i in range(1, totalComponents) if componentsStats[i][4] >= minArea]
    outImage = np.where(np.isin(labeledPixels, remaining_comp_labels) == True, 255, 0).astype(np.uint8)
    return outImage

让我们检查一些结果。对于第一个图像，这是k（黑色）频道：

这是预滤波的二进制图像（OTSU + bias）：

这是过滤的图像：

teserract returns this:

Text is: 820065084250

The strings returned for every image, according to Teserract, are:

Tesseract OCR
820065084250
930023482930
820065085833
930023485203
820065072022
930023485564
820065084802
820065084691
820065084730
930023445422
820065084551
82006507 1840

Note that the 2在中820065084551已成功识别，即使该数字被部分剪切。最后一个字符串中有空白，可能是因为图像上的数字有点分开。您可以后处理字符串以删除这些白色空间。