I am trying to extract the characters in the x-ray, I have tried using pytesseract to extract but couldn't succeed, I used a canny edge to remove the noise and extract, but still, I am not able to extract the text/chars. Can you please help/guide me to extract the text/chars
Try this tuotrial to locate the text:
https://www.pyimagesearch.com/2018/08/20/opencv-text-detection-east-text-detector/
Then once you locate you can isolate and use tesseract to recognize it.
If it's a DICOM file, you could use gdcm to get the attribute. It's available on python too.
pytesseract should be sufficient, if the file is in 'png' or 'jpg' form.
now suppose image is the name of your image. Please write the below code.
from PIL import Image
from pytesseract import image_to_string
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r'C:/Program Files (x86)/Tesseract-OCR/tesseract.exe'
im = Image.open('F:/kush/invert.jpg')
pytesseract.image_to_string(im, lang = 'eng')
Related
import pytesseract
from pdf2image import convert_from_path, convert_from_bytes
import cv2,numpy
def pil_to_cv2(image):
open_cv_image = numpy.array(image)
return open_cv_image[:, :, ::-1].copy()
path='OriginalsFile.pdf'
images = convert_from_path(path)
cv_h=[pil_to_cv2(i) for i in images]
img_header = cv_h[0][:160,:]
#print(pytesseract.image_to_string(Image.open('test.png'))) I only found this in tesseract docs
Hello, is there a way to read the img_header directly using pytesseract without saving it,
pytesseract docs
pytesseract.image_to_string() input format
As documentation explains pytesseract.image_to_string() needs a PIL image as input.
So you can convert your CV image into PIL one easily, like this:
from PIL import Image
... (your code)
print(pytesseract.image_to_string(Image.fromarray(img_header)))
if you really don't want to use PIL!
see:
https://github.com/madmaze/pytesseract/blob/master/src/pytesseract.py
pytesseract is an easy wrapper to run the tesseract command def run_and_get_output() line, you'll see that it saves your image into an temporary file, and then gives the address to the tesseract to run.
hence, you can do the same with opencv, just rewrite the pytesseract only .py file to do it with opencv, although; i don't see any performance improvements whatsoever.
The fromarray function allows you to load the PIL document into tesseract without saving the document to disk, but you should also ensure that you don`t send a list of pil images into tesseract. The convert_from_path function can generate a list of pil images if a pdf document contains multiple pages, therefore you need to send each page into tesseract individually.
import pytesseract
from pdf2image import convert_from_path
import cv2, numpy
def pil_to_cv2(image):
open_cv_image = numpy.array(image)
return open_cv_image[:, :, ::-1].copy()
doc = convert_from_path(path)
for page_number, page_data in enumerate(doc):
cv_h= pil_to_cv2(page_data)
img_header = cv_h[:160,:]
print(f"{page_number} - {pytesseract.image_to_string(Image.fromarray(img_header))}")
I am trying to ready Energy Efficiency Rating from EPC certificate using python. Usually EPC certificate comes in PDF format. I have converted PDF into image already and using pytesseract to get text from image. However I am not getting expected results.
Sample Image:
Expected output:
Current rating : 79, Potential rating : 79
What I have tried so far:
from pdf2image import convert_from_path
import pytesseract
from PIL import Image
pages = convert_from_path(r'my_file.pdf', 500)
img =pages[0].save(r'F:\Freelancer\EPC rating\fwdepcs\out.jpg', 'JPEG')
text = pytesseract.image_to_string(Image.open(r'F:\Freelancer\EPC rating\fwdepcs\out.jpg'))
However text does not capture 79.
I also tried cv2 pattern matching and shape detection, but those not worked for other reasons.
You say that you have convert this pdf to image file.
Use PIL(.crop()) or opencv to crop picture.And crop it like this:
And use PIL Image.convert("1"),maybe tesseract can catch this number.
If not,I think you can use jTessBoxEditor to train tesseract.
I am new to Pytesseract and currently in learning phase.
Here is the image:
from which I am trying to extract the text. The smaller text "movlmento, destmo" that I thought would be difficult to extract was easily extracted but the more bigger and visible text "TO" is not being extracted. I have searched all over but was not able to figure out a solution. Any help would be appreciated. Thank You!
Here is my code:
from PIL import Image
import pytesseract as p
p.pytesseract.tesseract_cmd = r"C:\Program Files (x86)\Tesseract-OCR\tesseract.exe"
img = Image.open('sample.jpg')
img = img.convert('1', dither=Image.NONE)
img2 = p.image_to_string(img)
print(img2)
how to read this png file using tesseract?
data:image/png;base64,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
Decoding Captcha isn't easy job, but maybe this example would be helpful.
In order to recognize text from Captcha you need more magic with cv2.
bfile='blablabla' #your base64 image
ffile=bfile.decode('base64')
pl=open('capcha.png','wb')
pl.write(ffile)
pl.close()
import pytesseract
from PIL import Image
img=Image.open('capcha.png')
text = pytesseract.image_to_string(img)
print (text)
i'm trying to use Wand to create a multi-size ico, but i don't find anything talking about that, only normal conversion, to ico... i've found "Sequences":
https://wand.readthedocs.org/en/latest/roadmap.html
and sequences look like what i need, but i only see samples trying to read the multiple images, but not how to create, am i missing something? or is not possible?
or is it possible to do using PIL/PILLOW?
You can append() a single image to Image.sequence list. For example:
from wand.color import Color
from wand.image import Image
with Image(width=32, height=32, background=Color('red')) as ico:
with Image(width=16, height=16, background=Color('green')) as s16:
ico.sequence.append(s16)
ico.save(filename='multisized.ico')
Result (multisized.ico):
I had a similar problem, but with creating a multi-page PDF from multiple JPEG files. In Imagemagick i used command -adjoin. In Wand i did the following:
from glob import glob
from wand.image import Image
files = glob('*.jpg')
with Image() as orig: # create empty Image object
for f in files:
page = Image(filename=f)
orig.sequence.append(page)
orig.save(filename='result.pdf')