Get white text on black background

Question

I want to identify text in a set of images. There are some images with both white and black colored text.

I used otsu thresholding to binarize image

After contour identification and removal of non text regions I identified the required text region.

I need all the text in white color. But I don't know how to do it. I thought of using a bitwise operator but couldn't find a method. Can someone help me with this?

Expected output:

import cv2
import numpy as np


def process(img):
 # read image
 img_no = str(img)
 rgb = cv2.imread(img_no + '.jpg')
 # cv2.imshow('original', rgb)

 # convert image to grayscale
 gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY)

 _, bw_copy = cv2.threshold(gray, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

 # bilateral filter
 blur = cv2.bilateralFilter(gray, 5, 75, 75)
 # cv2.imshow('blur', blur)

 # morphological gradient calculation
 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
 grad = cv2.morphologyEx(blur, cv2.MORPH_GRADIENT, kernel)
 # cv2.imshow('gradient', grad)

 # binarization
 _, bw = cv2.threshold(grad, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
 # cv2.imshow('otsu', bw)

 # closing
 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 1))
 closed = cv2.morphologyEx(bw, cv2.MORPH_CLOSE, kernel)
 # cv2.imshow('closed', closed)

 # finding contours
 contours, hierarchy = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

 mask = np.zeros(closed.shape, dtype=np.uint8)
 mask1 = np.zeros(bw_copy.shape, dtype=np.uint8)

 for idx in range(len(contours)):
    x, y, w, h = cv2.boundingRect(contours[idx])
    mask[y:y + h, x:x + w] = 0
    area = cv2.contourArea(contours[idx])
    aspect_ratio = float(w) / h
    cv2.drawContours(mask, contours, idx, (255, 255, 255), -1)
    r = float(cv2.countNonZero(mask[y:y + h, x:x + w])) / (w * h)

    # identify region of interest
    if r > 0.34 and 0.52 < aspect_ratio < 13 and area > 145.0:
        cv2.drawContours(mask1, [contours[idx]], -1, (255, 255, 255), -1)

 result = cv2.bitwise_and(bw_copy, mask1)
 cv2.imshow('result', result)

 print(img_no + " Done")
 cv2.waitKey()

New Image

Accepted answer doesn't work with this picture.

Answer 1

INPUT IMAGE:

I did the following:

import cv2 as cv
import numpy as np
img_path = '10002.png'
img = cv.imread(img_path)
img = cv.resize(img, (None, None), None, 0.4, 0.4)
img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
img = np.array(img, dtype='float32')
result = np.where(img == 0., 255, 0)
result = np.array(result, dtype='uint8')
result = cv.erode(result, kernel=np.ones(shape=(3, 3)), iterations=1)
cv.imshow('Image', img)
cv.imshow('Result', result)
cv.waitKey(0)
cv.destroyAllWindows()

OUTPUT IMAGE:

You can even invert the image using bitwise not and get black text on white background.