我正在制作一个读取身份证的 OCR。使用 YOLO 获取感兴趣的区域后，我将裁剪后的区域交给 Tesseract 来读取它。由于这些裁剪后的图像非常小且模糊，Tesseract 无法读取它们。当它can阅读它们，它会给出错误的预测。我认为通过提高裁剪图像的图像质量，这些问题都可以得到解决。
One of the cropped images:
我的问题是，我将如何改进这些图像？
@vasilisg 的回答。是一个非常好的解决方案。进一步改进的一种方法是使用形态学开放操作去除剩余的斑点。但是，这仅适用于小于图像中数字线宽的点。另一种选择是使用 openCV 连接组件模块删除少于 N 像素的“孤岛”。例如，您可以执行以下操作：
# External libraries used for
# Image IO
from PIL import Image
# Morphological filtering
from skimage.morphology import opening
from skimage.morphology import disk
# Data handling
import numpy as np
# Connected component filtering
import cv2
black = 0
white = 255
threshold = 160
# Open input image in grayscale mode and get its pixels.
img = Image.open("image.jpg").convert("LA")
pixels = np.array(img)[:,:,0]
# Remove pixels above threshold
pixels[pixels > threshold] = white
pixels[pixels < threshold] = black
# Morphological opening
blobSize = 1 # Select the maximum radius of the blobs you would like to remove
structureElement = disk(blobSize) # you can define different shapes, here we take a disk shape
# We need to invert the image such that black is background and white foreground to perform the opening
pixels = np.invert(opening(np.invert(pixels), structureElement))
# Create and save new image.
newImg = Image.fromarray(pixels).convert('RGB')
newImg.save("newImage1.PNG")
# Find the connected components (black objects in your image)
# Because the function searches for white connected components on a black background, we need to invert the image
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(np.invert(pixels), connectivity=8)
# For every connected component in your image, you can obtain the number of pixels from the stats variable in the last
# column. We remove the first entry from sizes, because this is the entry of the background connected component
sizes = stats[1:,-1]
nb_components -= 1
# Define the minimum size (number of pixels) a component should consist of
minimum_size = 100
# Create a new image
newPixels = np.ones(pixels.shape)*255
# Iterate over all components in the image, only keep the components larger than minimum size
for i in range(1, nb_components):
if sizes[i] > minimum_size:
newPixels[output == i+1] = 0
# Create and save new image.
newImg = Image.fromarray(newPixels).convert('RGB')
newImg.save("newImage2.PNG")
在此示例中，我执行了开运算和连通分量方法，但是如果使用连通分量方法，通常可以省略开运算。
结果如下：
After thresholding and opening:
After thresholding, opening and connected component filtering:
