Read what number the colored number image is to console

Instead of using Template Matching, a better approach is to use Pytesseract OCR to read the number with image_to_string(). But before performing OCR, you need to preprocess the image. For optimal OCR performance, the preprocessed image should have the desired text/number/characters to OCR in black with the background in white. A simple preprocessing step is to convert the image to grayscale, Otsu's threshold to obtain a binary image, then invert the image. Here's a visualization of the preprocessing step:

Input image -> Grayscale -> Otsu's threshold -> Inverted image ready for OCR

Result from Pytesseract OCR

2

Here's the results with the other images:

2

5

We use the --psm 6 configuration option to assume a single uniform block of text. See here for more configuration options.

Code

import cv2
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Load image, grayscale, Otsu's threshold, then invert
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
invert = 255 - thresh

# Perfrom OCR with Pytesseract
data = pytesseract.image_to_string(invert, lang='eng', config='--psm 6')
print(data)

cv2.imshow('thresh', thresh)
cv2.imshow('invert', invert)
cv2.waitKey()

Note: If you insist on using Template Matching, you need to use scale variant template matching. Take a look at how to isolate everything inside of a contour, scale it, and test the similarity to an image? and Python OpenCV line detection to detect X symbol in image for some examples. If you know for certain that your images are blue, then another approach would be to use color thresholding with cv2.inRange() to obtain a binary mask image then apply OCR on the image.

Given the lovely regular input, I expect that all you need is simple comparison to templates. Since you neglected to supply your code and output, it's hard to tell what might have gone wrong.

Very simply ...

• Rescale your input to the size or your templates.
• Calculate any straightforward matching evaluation on the input with each of the 10 templates. A simply matching count should suffice: how many pixels match between the two images.
• The template with the highest score is the identification.

You might also want to set a lower threshold for declaring a match, perhaps based on how well that template matches each of the other templates: any identification has to clearly exceed the match between two different templates.