extracting text information from a national id
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
}
I'm trying to do OCR arabic on the following ID but I get a very noisy picture, and can't extract information from it.
Here is my attempt
import tesserocr
from PIL import Image
import pytesseract
import matplotlib as plt
import cv2
import imutils
import numpy as np
image = cv2.imread(r'c:ahmedahmed.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray,11,18,18)
gray = cv2.GaussianBlur(gray,(5,5), 0)
kernel = np.ones((2,2), np.uint8)
gray = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,11,2)
#img_dilation = cv2.erode(gray, kernel, iterations=1)
#cv2.imshow("dilation", img_dilation)
cv2.imshow("gray", gray)
text = pytesseract.image_to_string(gray, lang='ara')
print(text)
with open(r"c:ahmedfile.txt", "w", encoding="utf-8") as myfile:
myfile.write(text)
cv2.waitKey(0)
result
sample
python opencv computer-vision tesseract
add a comment |
I'm trying to do OCR arabic on the following ID but I get a very noisy picture, and can't extract information from it.
Here is my attempt
import tesserocr
from PIL import Image
import pytesseract
import matplotlib as plt
import cv2
import imutils
import numpy as np
image = cv2.imread(r'c:ahmedahmed.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray,11,18,18)
gray = cv2.GaussianBlur(gray,(5,5), 0)
kernel = np.ones((2,2), np.uint8)
gray = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,11,2)
#img_dilation = cv2.erode(gray, kernel, iterations=1)
#cv2.imshow("dilation", img_dilation)
cv2.imshow("gray", gray)
text = pytesseract.image_to_string(gray, lang='ara')
print(text)
with open(r"c:ahmedfile.txt", "w", encoding="utf-8") as myfile:
myfile.write(text)
cv2.waitKey(0)
result
sample
python opencv computer-vision tesseract
are you only interested in the text that is in the red box?
– yapws87
Nov 29 '18 at 3:25
add a comment |
I'm trying to do OCR arabic on the following ID but I get a very noisy picture, and can't extract information from it.
Here is my attempt
import tesserocr
from PIL import Image
import pytesseract
import matplotlib as plt
import cv2
import imutils
import numpy as np
image = cv2.imread(r'c:ahmedahmed.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray,11,18,18)
gray = cv2.GaussianBlur(gray,(5,5), 0)
kernel = np.ones((2,2), np.uint8)
gray = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,11,2)
#img_dilation = cv2.erode(gray, kernel, iterations=1)
#cv2.imshow("dilation", img_dilation)
cv2.imshow("gray", gray)
text = pytesseract.image_to_string(gray, lang='ara')
print(text)
with open(r"c:ahmedfile.txt", "w", encoding="utf-8") as myfile:
myfile.write(text)
cv2.waitKey(0)
result
sample
python opencv computer-vision tesseract
I'm trying to do OCR arabic on the following ID but I get a very noisy picture, and can't extract information from it.
Here is my attempt
import tesserocr
from PIL import Image
import pytesseract
import matplotlib as plt
import cv2
import imutils
import numpy as np
image = cv2.imread(r'c:ahmedahmed.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray,11,18,18)
gray = cv2.GaussianBlur(gray,(5,5), 0)
kernel = np.ones((2,2), np.uint8)
gray = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,11,2)
#img_dilation = cv2.erode(gray, kernel, iterations=1)
#cv2.imshow("dilation", img_dilation)
cv2.imshow("gray", gray)
text = pytesseract.image_to_string(gray, lang='ara')
print(text)
with open(r"c:ahmedfile.txt", "w", encoding="utf-8") as myfile:
myfile.write(text)
cv2.waitKey(0)
result
sample
python opencv computer-vision tesseract
python opencv computer-vision tesseract
asked Nov 22 '18 at 13:36
chris burgeeschris burgees
155
155
are you only interested in the text that is in the red box?
– yapws87
Nov 29 '18 at 3:25
add a comment |
are you only interested in the text that is in the red box?
– yapws87
Nov 29 '18 at 3:25
are you only interested in the text that is in the red box?
– yapws87
Nov 29 '18 at 3:25
are you only interested in the text that is in the red box?
– yapws87
Nov 29 '18 at 3:25
add a comment |
2 Answers
2
active
oldest
votes
This is my output using ImageMagick TextCleaner script:
Script: textcleaner -g -e stretch -f 50 -o 30 -s 1 C:/Users/PC/Desktop/id.jpg C:/Users/PC/Desktop/out.png
Take a look here if you want to install and use TextCleaner script on Windows... It's a tutorial I made as simple as possible after few researches I made when I was in your same situation.
Now it should be very easy to detect the text and (not sure how simple) recognize it.
add a comment |
The text for your id is in black color which makes the extraction process easy. All you need to do is threshold the dark pixels and you should be able to get the text out.
Here is a snip of the code
import cv2
import numpy as np
# load image in grayscale
image = cv2.imread('AVXjv.jpg',0)
# remove noise
dst = cv2.blur(image,(3,3))
# extract dark regions which corresponds to text
val, dst = cv2.threshold(dst,80,255,cv2.THRESH_BINARY_INV)
# morphological close to connect seperated blobs
dst = cv2.dilate(dst,None)
dst = cv2.erode(dst,None)
cv2.imshow("dst",dst)
cv2.waitKey(0)
And here is the result:
There is a green text up which is not there in the thresholded picture
– chris burgees
Nov 29 '18 at 22:26
You can try processing the different channels separately to extract the green text.
– Qidi
Nov 30 '18 at 8:36
@Qidi Can you post an answer with your approach ?
– chris burgees
Nov 30 '18 at 10:09
Why would you need the green text? Isn't it the same for all the ids?
– yapws87
Dec 2 '18 at 1:44
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53432201%2fextracting-text-information-from-a-national-id%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
This is my output using ImageMagick TextCleaner script:
Script: textcleaner -g -e stretch -f 50 -o 30 -s 1 C:/Users/PC/Desktop/id.jpg C:/Users/PC/Desktop/out.png
Take a look here if you want to install and use TextCleaner script on Windows... It's a tutorial I made as simple as possible after few researches I made when I was in your same situation.
Now it should be very easy to detect the text and (not sure how simple) recognize it.
add a comment |
This is my output using ImageMagick TextCleaner script:
Script: textcleaner -g -e stretch -f 50 -o 30 -s 1 C:/Users/PC/Desktop/id.jpg C:/Users/PC/Desktop/out.png
Take a look here if you want to install and use TextCleaner script on Windows... It's a tutorial I made as simple as possible after few researches I made when I was in your same situation.
Now it should be very easy to detect the text and (not sure how simple) recognize it.
add a comment |
This is my output using ImageMagick TextCleaner script:
Script: textcleaner -g -e stretch -f 50 -o 30 -s 1 C:/Users/PC/Desktop/id.jpg C:/Users/PC/Desktop/out.png
Take a look here if you want to install and use TextCleaner script on Windows... It's a tutorial I made as simple as possible after few researches I made when I was in your same situation.
Now it should be very easy to detect the text and (not sure how simple) recognize it.
This is my output using ImageMagick TextCleaner script:
Script: textcleaner -g -e stretch -f 50 -o 30 -s 1 C:/Users/PC/Desktop/id.jpg C:/Users/PC/Desktop/out.png
Take a look here if you want to install and use TextCleaner script on Windows... It's a tutorial I made as simple as possible after few researches I made when I was in your same situation.
Now it should be very easy to detect the text and (not sure how simple) recognize it.
edited Nov 28 '18 at 17:07
answered Nov 28 '18 at 16:58
LinkLink
995634
995634
add a comment |
add a comment |
The text for your id is in black color which makes the extraction process easy. All you need to do is threshold the dark pixels and you should be able to get the text out.
Here is a snip of the code
import cv2
import numpy as np
# load image in grayscale
image = cv2.imread('AVXjv.jpg',0)
# remove noise
dst = cv2.blur(image,(3,3))
# extract dark regions which corresponds to text
val, dst = cv2.threshold(dst,80,255,cv2.THRESH_BINARY_INV)
# morphological close to connect seperated blobs
dst = cv2.dilate(dst,None)
dst = cv2.erode(dst,None)
cv2.imshow("dst",dst)
cv2.waitKey(0)
And here is the result:
There is a green text up which is not there in the thresholded picture
– chris burgees
Nov 29 '18 at 22:26
You can try processing the different channels separately to extract the green text.
– Qidi
Nov 30 '18 at 8:36
@Qidi Can you post an answer with your approach ?
– chris burgees
Nov 30 '18 at 10:09
Why would you need the green text? Isn't it the same for all the ids?
– yapws87
Dec 2 '18 at 1:44
add a comment |
The text for your id is in black color which makes the extraction process easy. All you need to do is threshold the dark pixels and you should be able to get the text out.
Here is a snip of the code
import cv2
import numpy as np
# load image in grayscale
image = cv2.imread('AVXjv.jpg',0)
# remove noise
dst = cv2.blur(image,(3,3))
# extract dark regions which corresponds to text
val, dst = cv2.threshold(dst,80,255,cv2.THRESH_BINARY_INV)
# morphological close to connect seperated blobs
dst = cv2.dilate(dst,None)
dst = cv2.erode(dst,None)
cv2.imshow("dst",dst)
cv2.waitKey(0)
And here is the result:
There is a green text up which is not there in the thresholded picture
– chris burgees
Nov 29 '18 at 22:26
You can try processing the different channels separately to extract the green text.
– Qidi
Nov 30 '18 at 8:36
@Qidi Can you post an answer with your approach ?
– chris burgees
Nov 30 '18 at 10:09
Why would you need the green text? Isn't it the same for all the ids?
– yapws87
Dec 2 '18 at 1:44
add a comment |
The text for your id is in black color which makes the extraction process easy. All you need to do is threshold the dark pixels and you should be able to get the text out.
Here is a snip of the code
import cv2
import numpy as np
# load image in grayscale
image = cv2.imread('AVXjv.jpg',0)
# remove noise
dst = cv2.blur(image,(3,3))
# extract dark regions which corresponds to text
val, dst = cv2.threshold(dst,80,255,cv2.THRESH_BINARY_INV)
# morphological close to connect seperated blobs
dst = cv2.dilate(dst,None)
dst = cv2.erode(dst,None)
cv2.imshow("dst",dst)
cv2.waitKey(0)
And here is the result:
The text for your id is in black color which makes the extraction process easy. All you need to do is threshold the dark pixels and you should be able to get the text out.
Here is a snip of the code
import cv2
import numpy as np
# load image in grayscale
image = cv2.imread('AVXjv.jpg',0)
# remove noise
dst = cv2.blur(image,(3,3))
# extract dark regions which corresponds to text
val, dst = cv2.threshold(dst,80,255,cv2.THRESH_BINARY_INV)
# morphological close to connect seperated blobs
dst = cv2.dilate(dst,None)
dst = cv2.erode(dst,None)
cv2.imshow("dst",dst)
cv2.waitKey(0)
And here is the result:
answered Nov 29 '18 at 14:52
yapws87yapws87
1,023213
1,023213
There is a green text up which is not there in the thresholded picture
– chris burgees
Nov 29 '18 at 22:26
You can try processing the different channels separately to extract the green text.
– Qidi
Nov 30 '18 at 8:36
@Qidi Can you post an answer with your approach ?
– chris burgees
Nov 30 '18 at 10:09
Why would you need the green text? Isn't it the same for all the ids?
– yapws87
Dec 2 '18 at 1:44
add a comment |
There is a green text up which is not there in the thresholded picture
– chris burgees
Nov 29 '18 at 22:26
You can try processing the different channels separately to extract the green text.
– Qidi
Nov 30 '18 at 8:36
@Qidi Can you post an answer with your approach ?
– chris burgees
Nov 30 '18 at 10:09
Why would you need the green text? Isn't it the same for all the ids?
– yapws87
Dec 2 '18 at 1:44
There is a green text up which is not there in the thresholded picture
– chris burgees
Nov 29 '18 at 22:26
There is a green text up which is not there in the thresholded picture
– chris burgees
Nov 29 '18 at 22:26
You can try processing the different channels separately to extract the green text.
– Qidi
Nov 30 '18 at 8:36
You can try processing the different channels separately to extract the green text.
– Qidi
Nov 30 '18 at 8:36
@Qidi Can you post an answer with your approach ?
– chris burgees
Nov 30 '18 at 10:09
@Qidi Can you post an answer with your approach ?
– chris burgees
Nov 30 '18 at 10:09
Why would you need the green text? Isn't it the same for all the ids?
– yapws87
Dec 2 '18 at 1:44
Why would you need the green text? Isn't it the same for all the ids?
– yapws87
Dec 2 '18 at 1:44
add a comment |
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53432201%2fextracting-text-information-from-a-national-id%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
are you only interested in the text that is in the red box?
– yapws87
Nov 29 '18 at 3:25