Opencv: Crop out text areas from license

I have the below image of a single drivers license, I want to extract information about the drivers license, name, DOB etc. My thought process is to find a way to group them line by line, and crop out the single rectangle that contains name, license, etc for eng and ara. But I have failed woefully.

import cv2
import os
import numpy as np

scan_dir = os.path.dirname(__file__)
image_dir = os.path.join(scan_dir, '../../images')


class Loader(object):
    def __init__(self, filename, gray=True):
        self.filename = filename
        self.gray = gray
        self.image = None

    def _read(self, filename):
        rgba = cv2.imread(os.path.join(image_dir, filename))

        if rgba is None:
            raise Exception("Image not found")

        if self.gray:
            gray = cv2.cvtColor(rgba, cv2.COLOR_BGR2GRAY)

        return gray, rgba


    def __call__(self):
        return self._read(self.filename)


class ImageScaler(object):

    def __call__(self, gray, rgba, scale_factor = 2):
        img_small_gray = cv2.resize(gray, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_AREA)
        img_small_rgba = cv2.resize(rgba, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_AREA)


        return img_small_gray, img_small_rgba



class BoxLocator(object):
    def __call__(self, gray, rgba):
        # image_blur = cv2.medianBlur(gray, 1)
        ret, image_binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
        image_not = cv2.bitwise_not(image_binary)

        erode_kernel = np.ones((3, 1), np.uint8)
        image_erode = cv2.erode(image_not, erode_kernel, iterations = 5)

        dilate_kernel = np.ones((5,5), np.uint8)
        image_dilate = cv2.dilate(image_erode, dilate_kernel, iterations=5)


        kernel = np.ones((3, 3), np.uint8)
        image_closed = cv2.morphologyEx(image_dilate, cv2.MORPH_CLOSE, kernel)
        image_open = cv2.morphologyEx(image_closed, cv2.MORPH_OPEN, kernel)

        image_not = cv2.bitwise_not(image_open)
        image_not = cv2.adaptiveThreshold(image_not, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, -2)

        image_dilate = cv2.dilate(image_not, np.ones((2, 1)), iterations=1)
        image_dilate = cv2.dilate(image_dilate, np.ones((2, 10)), iterations=1)

        image, contours, heirarchy = cv2.findContours(image_dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        for contour in contours:
            x, y, w, h = cv2.boundingRect(contour)
            # if w > 30 and h > 10:
            cv2.rectangle(rgba, (x, y), (x + w, y + h), (0, 0, 255), 2)

        return image_dilate, rgba



def entry():
    loader = Loader('sample-004.jpg')
    # loader = Loader('sample-004.jpg')
    gray, rgba = loader()

    imageScaler = ImageScaler()
    image_scaled_gray, image_scaled_rgba = imageScaler(gray, rgba, 1)

    box_locator = BoxLocator()
    gray, rgba = box_locator(image_scaled_gray, image_scaled_rgba)

    cv2.namedWindow('Image', cv2.WINDOW_NORMAL)
    cv2.namedWindow('Image2', cv2.WINDOW_NORMAL)

    cv2.resizeWindow('Image', 600, 600)
    cv2.resizeWindow('Image2', 600, 600)

    cv2.imshow("Image2", rgba)
    cv2.imshow("Image", gray)

    cv2.moveWindow('Image', 0, 0)
    cv2.moveWindow('Image2', 600, 0)

    cv2.waitKey()
    cv2.destroyAllWindows()

When I run the above code I get the below segmentation. Which is not close to what I want

But below is what I want to achieve, for all input license

from Opencv: Crop out text areas from license