Python Extract number from simple Image

Question

I have the following image

lower = np.array([175, 125, 45], dtype="uint8")
upper = np.array([255, 255, 255], dtype="uint8")

mask = cv2.inRange(image, lower, upper)
img = cv2.bitwise_and(image, image, mask=mask)

plt.figure()
plt.imshow(img)
plt.axis('off')
plt.show()

now if I try to transform into grayscale like this:

gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

I get that:

And I would like to extract the number on it.

The suggestion:

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
plt.imshow(gauss)

gives the image:

Then using pytesseract on any of the images:

data = pytesseract.image_to_string(img, config='outputbase digits')

gives:

'\x0c'

Another suggested solution is:

gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
thr = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV)[1]
txt = pytesseract.image_to_string(thr)
plt.imshow(thr)

And this gives

'\x0c'

Not very satisfying... Anyone has a better solution please?

Thanks!

Answer 1

I have a two step solution

---

• 1. Apply thresholding
• 2. Set psm mode to 7.

When you apply thresholding to the image:

Thresholding is a simplest method of displaying the features of the image.

Now from the output image, when we read:

txt = image_to_string(thr, config="--psm 7")
print(txt)

Result will be:

| 1,625 |

Now why do we set page-segmentation-mode (psm) mode to the 7?

Well, treating image as a single text line will give the accurate result.

But we have to modify the result. Since the current result is | 1,625 |

We should remove the |

print("".join([t for t in txt if t != '|']))

Result:

1,625

Code:

---

import cv2
from pytesseract import image_to_string

img = cv2.imread("LZ3vi.png")
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.threshold(gry, 0, 255,
                    cv2.THRESH_BINARY_INV)[1]
txt = image_to_string(thr, config="--psm 7")
print("".join([t for t in txt if t != '|']).strip())

Update

---

how do you get this clean black and white image from my original image?

Using 3-steps

• 1. Reading the image using opencv's imread function

  • img = cv2.imread("LZ3vi.png")
    

  • Now we read the image in BGR fashion. (Not RGB)

• 2. Convert the image to the graysclae

  • gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    

  • Result will be:

    • 
• 3. Apply threshold

  • thr = cv2.threshold(gry, 0, 255, cv2.THRESH_BINARY_INV)[1]
    

  • Result will be:

    • 

Now if you are wondering about thresholding. Read the simple-threhsolding

All my filters, grayscale... get weird colored images

The reason is, when you are displaying the image using pyplot, you need to set color-map (cmap) to gray

plt.imshow(img, cmap='gray')

You can read the other types here

Answer 2

Two issues blocked the pytessract from detecting your number:

1. The white rectangle around the number(Inverting and filling is the solution).
2. The Noise in the numbers shape(Gaussian Smoothing dealt with that)

The solution that AlexAlex has proposed will work perfectly if it was followed by a Gaussian filter:

output: 1,625

import numpy as np
import pytesseract
import cv2

BGR = cv2.imread('11.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)