如何对文本图像进行偏斜校正，同时也检索该图像的新边界框? [英] How to de-skew a text image also retrieve the new bounding box of that image?

问题描述

这是我收到的收据图像，我已使用matplotlib对其进行了绘制，如果您看到该图像，则其中的文本不直.我该如何校正和修复它?

Here's a receipt image that I've got and I've plotted it using matplotlib and If you see the image the text in it is not straight. How can I de-skew and fix it?

from skimage import io
import cv2

# x1, y1, x2, y2, x3, y3, x4, y4
bbox_coords = [[20, 68], [336, 68], [336, 100], [20, 100]]

image = io.imread('https://i.ibb.co/3WCsVBc/test.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

fig, ax = plt.subplots(figsize=(20, 20))
ax.imshow(gray, cmap='Greys_r')

# for plotting bounding box uncomment the two lines below
#rect = Polygon(bbox_coords, fill=False, linewidth=1, edgecolor='r')
#ax.add_patch(rect)
plt.show()

print(gray.shape)
(847, 486)

我想，如果我们想先倾斜，就必须找到边缘，因此我尝试使用canny算法来找到边缘，然后获得如下轮廓.

I think if we want to de-skew first we have to find the edges, so I tried to find the edges using canny algorithm and then get contours like below.

from skimage import filters, feature, measure

def edge_detector(image):
    image = filters.gaussian(image, 2, mode='reflect')
    edges = feature.canny(image)
    contours = measure.find_contours(edges, 0.8)
    return edges, contours

fig, ax = plt.subplots(figsize=(20, 20))

ax.imshow(gray, cmap='Greys_r'); 
gray_image, contours = edge_detector(gray)

for n, contour in enumerate(contours):
    ax.plot(contour[:, 1], contour[:, 0], linewidth=2)

我从上面的代码中获得的边缘是每个文本的边缘，但这不是我所需要的.我需要正确收据的边缘吗?

The edges that I've got from above code is the edges of each text but that is not what I needed. I need to get edges of receipt right?

我还需要一种方法来使图像倾斜(即拉直图像)后获得新的边界框坐标?

Also I need a way to get the new bounding box coordinates after de-skewing the image (i.e straightening the image)?

如果有人在解决类似问题，请帮帮我吗?谢谢.

If anyone has worked on similar problem please help me out? Thanks.

推荐答案

这是Projection Profile Method的修改实现，如

Here's a modified implementation of the Projection Profile Method to correct skewed images as described in Projection profile based skew estimation algorithm for JBIG compressed images. After obtaining a binary image, the idea is to rotate the image at various angles and generate a histogram of pixels in each iteration. To determine the skew angle, we compare the maximum difference between peaks and using this skew angle, rotate the image to correct the skew. The amount of peaks to determine can be adjusted by the delta value, the lower the delta, the more peaks will be checked with the tradeoff that the process will take longer.

在->之后

代码

import cv2
import numpy as np
from scipy.ndimage import interpolation as inter

def correct_skew(image, delta=.1, limit=5):
    def determine_score(arr, angle):
        data = inter.rotate(arr, angle, reshape=False, order=0)
        histogram = np.sum(data, axis=1)
        score = np.sum((histogram[1:] - histogram[:-1]) ** 2)
        return histogram, score

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur = cv2.medianBlur(gray, 3)
    thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] 

    scores = []
    angles = np.arange(-limit, limit + delta, delta)
    for angle in angles:
        histogram, score = determine_score(thresh, angle)
        scores.append(score)

    best_angle = angles[scores.index(max(scores))]

    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, best_angle, 1.0)
    rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, \
              borderMode=cv2.BORDER_REPLICATE)

    return best_angle, rotated

if __name__ == '__main__':
    image = cv2.imread('1.jpg')
    angle, rotated = correct_skew(image)
    print(angle)
    cv2.imshow('rotated', rotated)
    cv2.imwrite('rotated.png', rotated)
    cv2.waitKey()

注意:还请查看

Note: Also take a look at rotated skewed image to upright position for another approach

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