Tesseract OCR having trouble detecting numbers

Question

I am trying to detect some numbers with tesseract in python. Below you will find my starting image and what I can get it down to. Here is the code I used to get it there.

import pytesseract
import cv2
import numpy as np
pytesseract.pytesseract.tesseract_cmd = "C:\\Users\\choll\\AppData\\Local\\Programs\\Tesseract-OCR\\tesseract.exe"

image = cv2.imread(r'64normalwart.png')
lower = np.array([254, 254, 254])
upper = np.array([255, 255, 255])
image = cv2.inRange(image, lower, upper)
image = cv2.bitwise_not(image)
#Uses a language that should work with minecraft text, I have tried with and without, no luck 
text = pytesseract.image_to_string(image, lang='mc')
print(text)
cv2.imwrite("Wartthreshnew.jpg", image)
cv2.imshow("Image", image)
cv2.waitKey(0)

I end up with black numbers on a white background which seems pretty good but tesseract can still not detect the numbers. I also noticed the numbers were pretty jagged but I don't know how to fix that. Does anyone have recommendations for how I could make tesseract be able to recognize these numbers?

Starting Image

What I end up with

Answer 1

Your problem is with the page segmentation mode. Tesseract segments every image in a different way. When you don't choose an appropriate PSM, it goes for mode 3, which is automatic and might not be suitable for your case. I've just tried your image and it works perfectly with PSM 6.

df = pytesseract.image_to_string(np.array(image),lang='eng', config='--psm 6')

These are all PSMs availabe at this moment:

  0    Orientation and script detection (OSD) only.
  1    Automatic page segmentation with OSD.
  2    Automatic page segmentation, but no OSD, or OCR.
  3    Fully automatic page segmentation, but no OSD. (Default)
  4    Assume a single column of text of variable sizes.
  5    Assume a single uniform block of vertically aligned text.
  6    Assume a single uniform block of text.
  7    Treat the image as a single text line.
  8    Treat the image as a single word.
  9    Treat the image as a single word in a circle.
 10    Treat the image as a single character.
 11    Sparse text. Find as much text as possible in no particular order.
 12    Sparse text with OSD.
 13    Raw line. Treat the image as a single text line,
            bypassing hacks that are Tesseract-specific.