Fetching text specifically digits from an image OpenCV - python-3.x

Question

I am trying to fetch label 0026 from the attached image:
Initial input image
I tried below code initially to fetch the text:
import pytesseract
BGR = cv2.imread('C:/Users/Choudharyp/CV/11952/01_A_parcel_layer_single_parcel.png')
RGB = cv2.cvtColor(BGR, cv2.COLOR_BGR2RGB)
lower = np.array([175, 125, 45], dtype="uint8")
upper = np.array([255, 255, 255], dtype="uint8")
mask = cv2.inRange(RGB, lower, upper)
img = cv2.bitwise_and(RGB, RGB, mask=mask)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gray = 255 - gray
emp = np.full_like(gray, 255)
emp -= gray
emp[emp==0] = 255
emp[emp<100] = 0
gauss = cv2.GaussianBlur(emp, (3,3), 1)
gauss[gauss<220] = 0
text = pytesseract.image_to_string(gauss, config='outputbase digits')
print("Text=>",text)
This did not work possibly because I need to remove green lines from the image.
Hence I first wrote below code to remove green lines from the image and extract only the black colors in the image (this works fine):
# Imports
import cv2
import numpy as np
import pytesseract
# Read image
imagePath = "C:/Users/Choudharyp/CV/11952/" #insert your own loctaion
inputImage = cv2.imread(imagePath + "01_A_parcel_layer_single_parcel.png")
# Conversion to CMYK (just the K channel):
# Convert to float and divide by 255:
imgFloat = inputImage.astype(np.float64) / 255.
# Calculate channel K:
kChannel = 1 - np.max(imgFloat, axis=2)
# Convert back to uint 8:
kChannel = (255 * kChannel).astype(np.uint8)
# Threshold image:
binaryThresh = 190
_, binaryImage = cv2.threshold(kChannel, binaryThresh, 255, cv2.THRESH_BINARY)
cv2.imshow('Black_LettersOnly', binaryImage)
cv2.waitKey(0)
Output looks like below :
Image with only black label
The label 0026 however is too small in the image.
Then, I used the same code as above to fetch the text from the image, however it still doesn't work. Can someone suggest what else I could do to start fetching the labels from the image ?

Answer 1

In binaryImage you get white text on black background. However, Tesseract is optimized to detect dark text on bright background and apparently it works perfectly fine in this case, when you simply invert your image:
...
text = pytesseract.image_to_string(255-binaryImage, config='outputbase digits')
print("Text=>", text.strip())
>>> Text=> 0026
However, if you want it even simpler, I'd prefer HSV color space and threshold the value channel with a one-liner to maintain just the non-colored/black pixels:
...
inputImageHSV = cv2.cvtColor(inputImage, cv2.COLOR_BGR2HSV)
binaryImage = (inputImageHSV[..., 2] > 120).astype(np.uint8) * 255
text = pytesseract.image_to_string(binaryImage, config='outputbase digits')
print("Text=>", text.strip())
>>> Text=> 0026