Detect and fix text skew

Based on your above comment, here is the code based on the tutorial here, working fine for the above image,

Source

Rotated

 Mat src=imread("text.png",0);
 Mat thr,dst;
 threshold(src,thr,200,255,THRESH_BINARY_INV);
 imshow("thr",thr);

  std::vector<cv::Point> points;
  cv::Mat_<uchar>::iterator it = thr.begin<uchar>();
  cv::Mat_<uchar>::iterator end = thr.end<uchar>();
  for (; it != end; ++it)
    if (*it)
      points.push_back(it.pos());

  cv::RotatedRect box = cv::minAreaRect(cv::Mat(points));
  cv::Mat rot_mat = cv::getRotationMatrix2D(box.center, box.angle, 1);

  //cv::Mat rotated(src.size(),src.type(),Scalar(255,255,255));
  Mat rotated;
  cv::warpAffine(src, rotated, rot_mat, src.size(), cv::INTER_CUBIC);
 imshow("rotated",rotated);

Edit:

Also see the answer here , might be helpful.

Here's a Python 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

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Input

Result

Detected skew angle: -5

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()