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Get numbers from cropped image pytesseract

I have a cropped image and I am trying to get the numbers on that cropped image
Here's the code I am using
image = cv2.imread('Cropped.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
invert = 255 - opening
data = pytesseract.image_to_string(invert, lang='eng', config='--psm 6')
print(data)
Here's the sample cropped image
All what I got some numbers and not all of them. How to enhance such an image to be able to extract only the numbers?
I tried the code on this image but doesn't return correct numbers

You can easily solve this with three-main steps
Upsampling
Applying simple-threshold
set configuration to digits
Upsampling for accurate recognition. Otherwise tesseract may misterpret the digits.
Threshold Displays only the features of the image.
**Configuration Setting will recognize the digits
Result
Upsampling
Threshold
Pytesseract
277032200746
Code:
import cv2
import pytesseract
img1 = cv2.imread("kEpyN.png") # "FX2in.png"
gry1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
(h, w) = gry1.shape[:2]
gry1 = cv2.resize(gry1, (w*2, h*2))
thr1 = cv2.threshold(gry1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
txt1 = pytesseract.image_to_string(thr1, config="digits")
print("".join(t for t in txt1 if t.isalnum()))
cv2.imshow("thr1", thr1)
cv2.waitKey(0)
Update:
Most-probably a version mismatch causes extra words and digits.
One-way to solving is taking a range of the image
For instance, from the thresholded image:
(h_thr, w_thr) = thr1.shape[:2]
thr1 = thr1[0:h_thr-10, int(w_thr/2)-400:int(w_thr/2)+200]
Result will be:
Now if you read, result should be like this output
277032200746

Improving image pre-processing for tesseract (video game screenshot)

I am trying to read text for prices in a video game and am experiencing difficulty in pre-processing the image.
The rest of my code is "complete", as in after the text is extracted I am formatting it and outputting into CSV for later use.
This is what I have come up with so far for the following images, and would like input on other thresholds or pre-processing tools that will make the OCR more accurate.
Raw Image Screenshot
After gamma, denoise on left - binary threshold on right
The text detected
As you can see, it is very close but not perfect. I would like to make it more accurate as I will be processing many frames eventually.
Here is my current code:
import cv2
import pytesseract
import pandas as pd
import numpy as np
# Tells pytesseract where the tesseract environment is installed on local computer
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
img = cv2.imread("./image_frames/frame0.png")
# gamma to darken text to be same opacity?
def adjust_gamma(crop_img, gamma=1.0):
# build a lookup table mapping the pixel values [0, 255] to
# their adjusted gamma values
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255
for i in np.arange(0, 256)]).astype("uint8")
# apply gamma correction using the lookup table
return cv2.LUT(crop_img, table)
adjusted = adjust_gamma(crop_img, gamma=0.15)
# grayscale the image
gray = cv2.cvtColor(adjusted, cv2.COLOR_BGR2GRAY)
# denoising image
dst = cv2.fastNlMeansDenoising(gray, None, 10, 10, 10)
# binary threshold
thresh = cv2.threshold(gray, 35, 255, cv2.THRESH_BINARY_INV)[1]
# OCR configurations (3 is default)
config = "--psm 3"
# Just show the image
cv2.imshow("before", gray)
cv2.imshow("before", dst)
cv2.imshow("thresh", thresh)
cv2.waitKey(0)
# Reads text from the image and prints to console
text = pytesseract.image_to_string(thresh, config=config)
# remove double lines
text = text.replace('\n\n','\n')
# remove unicode character
text = text.replace('', '')
print(text)
Any help is appreciated as I am very new to this!

Step#1: Scale the image
Step#2: Apply adaptive-threshold
Step#3: Set page-segmentation-mode (psm) to 6 (Assume a single uniform block of text.)
1 Scaling the image:
The reason is to see the image clearly, since the original image is really small.
img = cv2.imread("udQw1.png")
img = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
2 Apply adaptive-threshold
Generally threshold is applied, but in your image, applying threshold has no effect to the result.
For different images you may need to set different C and block values.
For instance for the 1st image:
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 15, 22)
Result:
For instance for the 2nd image:
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 51, 4)
Result:
3 Set psm to 6 which assumes the image as a single uniform block of text.
txt = pytesseract.image_to_string(thr, config="--psm 6")
print(txt)
Result for the 1st image:
Dragon Claymore
1,388,888,888 mesos.
Maple Pyrope Spear
288,888,888 mesos.
Element Pierce
488,888,888 mesos.
Purple Adventurer Cape
97,777,777 mesos.
Result for the 2nd image:
Ring of Alchemist
749,999,995 mesos.
Dragon Slash Claw
499,999,995 mesos.
"Stormcaster Gloves
149,999,995 mesos.
Elemental Wand 6
749,999,995 mesos.
Big Money Chalr
1 tor 249,999,985 mesos.|
Code for the 1st image:
import pytesseract
import cv2
img = cv2.imread("udQw1.png")
img = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 15, 22)
txt = pytesseract.image_to_string(thr, config="--psm 6")
print(txt)
Code for the 2nd image:
import pytesseract
import cv2
img = cv2.imread("7Y2yx.png")
img = cv2.resize(img, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 51, 4)
txt = pytesseract.image_to_string(thr, config="--psm 6")
print(txt)
Links
Simple and adaptive-threhsold
Page segmentation Modes
Improving quality of the output

Can't get numbers from image with python, Tesseract and opencv

i have to get numbers from a water-meter image usign python tesseract and opencv.
I have tried to change the --psm but it's doesn't work.
Here the image without modification :
enter image description here
Here the outpout image :
enter image description here
I need your help guys, i'm starting python and i'm already blocked :'(
My code :
from PIL import Image
import pytesseract
import cv2
import numpy as np
import urllib
import requests
pytesseract.pytesseract.tesseract_cmd = r'C:\Users\Hymed\AppData\Local\Tesseract-OCR\tesseract.exe'
col = Image.open("pts.jpg")
gray = col.convert('L')
bw = gray.point(lambda x: 0 if x<128 else 255, '1')
bw.save("cp19.png")
image = cv2.imread('cp19.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = 255 - cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Blur and perform text extraction
thresh = cv2.GaussianBlur(thresh, (3,3), 0)
img1 = np.array(thresh)
data = pytesseract.image_to_string(img1, config='--psm 11 digits')
print(data)
cv2.imshow('thresh', thresh)
cv2.waitKey()

You have nearly finished the task.
I use the divide operation, after the GaussianBlur.
div = cv2.divide(gray, thresh, scale=192)
Result:
When I read from the image:
data = pytesseract.image_to_string(div, config='--psm 11 digits')
print(data)
Result:
00000161
Code: (Just added div = cv2.divide(gray, thresh, scale=192) rest are your code)
from PIL import Image
import pytesseract
import cv2
import numpy as np
col = Image.open("TOaEW.jpg")
gray = col.convert('L')
bw = gray.point(lambda x: 0 if x < 128 else 255, '1')
bw.save("cp19.png")
image = cv2.imread('cp19.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = 255 - cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Blur and perform text extraction
thresh = cv2.GaussianBlur(thresh, (3, 3), 0)
div = cv2.divide(gray, thresh, scale=192) # added
data = pytesseract.image_to_string(div, config='--psm 11 digits')
print(data)

I tried to read the number from an image using Tesseract. Except the numbers shown in the first line, it also returned an unidentified symbol in the second line. I don't understand what I did wrong. Here is the code and the results
code and output
This is the image I extracted the number from:
Image used for number extraction

opencv thresholding and pytesseract

currently I am trying to develop some simple computervision code to read the amount of kills that I have in a call of duty game and save it to an array as an integer. The code is screenshotting my screen every second and using opencv I am thresholding the image and inputting it into pytesseract. Although the numbers stay the same, the background noise changes the image a lot and forces a lot of null inputs. I am ok if it misses a few inputs but it misses %50 or more of all of the digits. If anyone has any tips on thresholding a single digit image with varying backgrounds, it would be a huge help.
'''
pytesseract.pytesseract.tesseract_cmd = r'C:/Program Files/Tesseract-OCR/tesseract'
pyautogui.screenshot('pictures/Kill.png', region = (1822, 48, 30, 23))
img = cv2.imread('pictures/Kill.png')
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh1 = cv2.threshold(img, 255, 255, cv2.THRESH_TRUNC)
cv2.imwrite('pictures/killthresh1.png',thresh1)
ret, thresh1 = cv2.threshold(img, 180, 255, cv2.THRESH_BINARY)
thresh1 = cv2.bitwise_not(thresh1)
cv2.imwrite('pictures/Killthresh2.png', thresh1)
custom_config = r'-l eng --oem 3 --psm 7 -c
tessedit_char_whitelist="1234567890" '
killnumber = pytesseract.image_to_string(thresh1, config = custom_config)
'''
Original pyautogui screenshot
TRUNC thresholded
BINARY thresholded
NOTE: These images yieled a 'NULL' result and I dont know why

After you read the image, img = cv2.imread('pictures/Kill.png')
Apply adaptive-threshold on Original pyautogui screenshot:
Now read:
txt = pytesseract.image_to_string(thr, config="--psm 7")
print(txt)
Result:
3
Code:
import cv2
import pytesseract
img = cv2.imread("0wHAy.png")
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.adaptiveThreshold(gry, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 21, 9)
txt = pytesseract.image_to_string(thr, config="--psm 7")
print(txt)

How to OCR image with Tesseract

I am starting to learn OpenCV and Tesseract, and have trouble with what seems to be a very simple example.
Here is an image that I am trying to OCR, that reads "171 m":
I do some preprocessing. Since blue is the dominant color of the text, I extract the blue channel and apply simple thresholding.
img = cv2.imread('171_m.png')[y, x, 0]
_, thresh = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY_INV)
The resulting image looks like this:
Then throw that into Tesseract, with psm 7 for single line:
text = pytesseract.image_to_string(thresh, config='--psm 7')
print(text)
>>> lim
I also tried to restrict possible characters, and it gets a bit better, but not quite.
text = pytesseract.image_to_string(thresh, config='--psm 7 -c tessedit_char_whitelist=1234567890m')
print(text)
>>> 17m
OpenCV v4.1.1.
Tesseract v5.0.0-alpha.20190708
Any help appreciated.

Before throwing the image into Pytesseract, preprocessing can help. The desired text should be in black while the background should be in white. Here's an approach
Convert image to grayscale and enlarge image
Gaussian blur
Otsu's threshold
Invert image
After converting to grayscale, we enlarge the image using imutils.resize() and Gaussian blur. From here we Otsu's threshold to get a binary image
If you have noisy images, an additional step would be to use morphological operations to smooth or remove noise. But since your image is clean enough, we can simply invert the image to get our result
Output from Pytesseract using --psm 6
171m
import cv2
import pytesseract
import imutils
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
image = cv2.imread('1.png',0)
image = imutils.resize(image, width=400)
blur = cv2.GaussianBlur(image, (7,7), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
result = 255 - thresh
data = pytesseract.image_to_string(result, lang='eng',config='--psm 6')
print(data)
cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.waitKey()

Disclaimer : This is not a solution, just a trial to partially solve this.
This process works only if you have knowledge of the number of the characters present in the image beforehand. Here is the trial code :
img0 = cv2.imread('171_m.png', 0)
adap_thresh = cv2.adaptiveThreshold(img0, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
text_adth = pytesseract.image_to_string(adap_thresh, config='--psm 7')
After adaptive thresholding, the produced image is like this :
Pytesseract gives output as :
171 mi.
Now, if you know, in advance, the number of characters present, you can slice the string read by pytesseract and get the desired output as '171m'.

I thought your image was not sharp enough, hence I applied the process described at How do I increase the contrast of an image in Python OpenCV to first sharpen your image and then proceed by extracting the blue layer and running the tesseract.
I hope this helps.
import cv2
import pytesseract
img = cv2.imread('test.png') #test.png is your original image
s = 128
img = cv2.resize(img, (s,int(s/2)), 0, 0, cv2.INTER_AREA)
def apply_brightness_contrast(input_img, brightness = 0, contrast = 0):
if brightness != 0:
if brightness > 0:
shadow = brightness
highlight = 255
else:
shadow = 0
highlight = 255 + brightness
alpha_b = (highlight - shadow)/255
gamma_b = shadow
buf = cv2.addWeighted(input_img, alpha_b, input_img, 0, gamma_b)
else:
buf = input_img.copy()
if contrast != 0:
f = 131*(contrast + 127)/(127*(131-contrast))
alpha_c = f
gamma_c = 127*(1-f)
buf = cv2.addWeighted(buf, alpha_c, buf, 0, gamma_c)
return buf
out = apply_brightness_contrast(img,0,64)
b, g, r = cv2.split(out) #spliting and using just the blue
pytesseract.image_to_string(255-b, config='--psm 7 -c tessedit_char_whitelist=1234567890m') # the 255-b here because the image has black backgorund and white numbers, 255-b switches the colors