This code extract the individual characters from the input image and output an image with combined character regions

recombine_img.py

import cv2
import matplotlib.pyplot as plt
from skimage import measure

####
# Ref to https://github.com/Vasistareddy/python-rlsa/blob/master/rlsa.py
import numpy as np

def iteration(image: np.ndarray, value: int) -> np.ndarray:
    """
    This method iterates over the provided image by converting 255's to 0's if the number of consecutive 255's are
    less the "value" provided
    """

    rows, cols = image.shape
    for row in range(0,rows):
        try:
            start = image[row].tolist().index(0) # to start the conversion from the 0 pixel
        except ValueError:
            start = 0 # if '0' is not present in that row

        count = start
        for col in range(start, cols):
            if image[row, col] == 0:
                if (col-count) <= value and (col-count) > 0:
                    image[row, count:col] = 0               
                count = col  
    return image 

def rlsa(image: np.ndarray, value: int = 0, horizontal: bool = True) -> np.ndarray:
    """
    rlsa(RUN LENGTH SMOOTHING ALGORITHM) is to extract the block-of-text or the Region-of-interest(ROI) from the
    document binary Image provided. Must pass binary image of ndarray type.
    """
    image = image.copy()
    if isinstance(image, np.ndarray): # image must be binary of ndarray type
        value = int(value) if value>=0 else 0 # consecutive pixel position checker value to convert 255 to 0
        try:
            # RUN LENGTH SMOOTHING ALGORITHM working horizontally on the image
            if horizontal:
                image = iteration(image, value)   

            # RUN LENGTH SMOOTHING ALGORITHM working vertically on the image
            else:
                image = image.T
                image = iteration(image, value)
                image = image.T

        except (AttributeError, ValueError) as e:
            image = None
            print("ERROR: ", e, "\n")
            print('Image must be an np ndarray and must be in "binary". Use Opencv/PIL to convert the image to binary.\n')
            print("import cv2;\nimage=cv2.imread('path_of_the_image');\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY);\n\
                (thresh, image_binary) = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n")
            print("method usage -- rlsa.rlsa(image_binary, True, False, 10)")
    else:
        print('Image must be an np ndarray and must be in binary')
        image = None
    return image
    
####

def obtain_contour(img):

    img = img.astype('uint8')*255
    contours, hierarchy = cv2.findContours(img, 1, 2)

    contours = sorted(contours, key = cv2.contourArea)
    x,y,w,h = cv2.boundingRect(contours[-1])
    return [x,y,w,h]

def recombine_img(img_binary, img_color):
    
    mask = rlsa(img_binary, 40, True)
    
    component_diagram = measure.label(255-mask)
    component_areas = np.bincount(component_diagram.ravel()) 
    # The first element is the background class
    
    img_width = img_color.shape[1]
    coords = []
    for component_id, area in enumerate(component_areas[1:], start=1):
        if area > 10:
            content_region = component_diagram==(component_id)
            x,y,w,h  = obtain_contour(content_region)
            coords.append((y-1, y+h+1))

    return np.vstack([img_color[coord[0]:coord[1]] for coord in coords])

if __name__ == "__main__":
    img_color = cv2.imread('0_1.png')
    img_gray  = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY)
    _, img_binary = cv2.threshold(img_gray, 0, 255, cv2.THRESH_OTSU)
    img_recombined = recombine_img(img_binary, img_color)
    
    plt.subplot(121)
    plt.imshow(img_color[..., ::-1])
    plt.subplot(122)
    plt.imshow(img_recombined[..., ::-1])