用于 OCR 的 Python OpenCV 偏斜校正

Python OpenCV skew correction for OCR(用于 OCR 的 Python OpenCV 偏斜校正)

问题描述

目前，我正在做一个 OCR 项目，我需要从标签上读取文本(参见下面的示例图片).我遇到了图像倾斜问题，我需要帮助修复图像倾斜，以便文本是水平的而不是倾斜的.目前，我正在使用的过程尝试从给定范围(代码包含在下面)中对不同角度进行评分，但这种方法不一致，有时会过度校正图像歪斜或完全无法识别歪斜并纠正它.请注意，在进行歪斜校正之前，我将所有图像旋转 270 度以使文本直立，然后通过下面的代码传递图像.传递给函数的图像已经是二值图像了.

Currently, I am working on an OCR project where I need to read the text off of a label (see example images below). I am running into issues with the image skew and I need help fixing the image skew so the text is horizontal and not at an angle. Currently the process I am using attempts to score different angles from a given range (code included below), but this method is inconsistent and sometimes overcorrects an image skew or flat out fails to identify the skew and correct it. Just as a note, before the skew correction I am rotating all of the images by 270 degrees to get the text upright, then I am passing the image through the code below. The image passed through to the function is already a binary image.

代码:

def findScore(img, angle):
    """
    Generates a score for the binary image recieved dependent on the determined angle.

    Vars:

    - array <- numpy array of the label

    - angle <- predicted angle at which the image is rotated by

    Returns:

    - histogram of the image
    - score of potential angle
    """
    data = inter.rotate(img, angle, reshape = False, order = 0)
    hist = np.sum(data, axis = 1)
    score = np.sum((hist[1:] - hist[:-1]) ** 2)
    return hist, score

def skewCorrect(img):
    """
    Takes in a nparray and determines the skew angle of the text, then corrects the skew and returns the corrected image.

    Vars:

    - img <- numpy array of the label

    Returns:

    - Corrected image as a numpy array

    """
    #Crops down the skewImg to determine the skew angle
    img = cv2.resize(img, (0, 0), fx = 0.75, fy = 0.75)

    delta = 1
    limit = 45
    angles = np.arange(-limit, limit+delta, delta)
    scores = []
    for angle in angles:
        hist, score = findScore(img, angle)
        scores.append(score)
    bestScore = max(scores)
    bestAngle = angles[scores.index(bestScore)]
    rotated = inter.rotate(img, bestAngle, reshape = False, order = 0)
    print("[INFO] angle: {:.3f}".format(bestAngle))
    #cv2.imshow("Original", img)
    #cv2.imshow("Rotated", rotated)
    #cv2.waitKey(0)
    
    #Return img
    return rotated

校正前后的标签示例图片

Example images of the label before correction and after

修正前->修正后

如果有人能帮我解决这个问题，那将有很大帮助.

If anyone can help me figure this problem out, it would be of much help.

推荐答案

这是一个用于确定偏斜的 Projection Profile Method 的实现.在获得二值图像后，想法是将图像旋转到各个角度，并在每次迭代中生成像素的直方图.为了确定倾斜角度，我们比较了峰值之间的最大差异，并使用这个倾斜角度，旋转图像来纠正倾斜

Here's an implementation of the Projection Profile Method to determine skew. After obtaining a binary image, the idea is 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

左(原始)，右(更正)

Left (original), Right (corrected)

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)
    thresh = cv2.threshold(gray, 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.png')
    angle, rotated = correct_skew(image)
    print(angle)
    cv2.imshow('rotated', rotated)
    cv2.imwrite('rotated.png', rotated)
    cv2.waitKey()

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