this is not my code, i found it into another question, i try it and work very well. This is the code:

import cv2
import os
import numpy as np

def findCenter(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    th, threshed = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)

    _, cnts, hierarchy = cv2.findContours(threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    M = cv2.moments(cnts[0])
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])
    return (cX, cY)

img1 = cv2.imread("Path of the image you want to copy")
img2 = cv2.imread("Path of the image you want to use like a backgroud")

pt1 = findCenter(img1)
pt2 = findCenter(img2)

## (2) Calc offset
dx = (pt1[0] - pt2[0])
dy = (pt1[1] - pt2[1])

h, w = img2.shape[:2]

dst = img1.copy()
dst[dy:dy + h, dx:dx + w] = img2

cv2.imwrite(path + roi, dst)

This is the original answer: Match center of two images (OpenCV, Python)

Are they all going to be letters (or even H's)?

There are a few ways to approach this. The fastest (but most naive) way would be to find the left-most and right-most black pixels, and then center at the halfway point. Then do the same vertical. Basically create a bounding box to your image, where you filter on anything that isn't #FFFFFF

Again, depends on the data though.