Using the below code I am able to read all text in an image:
import cv2
img = cv2.imread(r'/<path_to_image>/text.png')
print(pytesseract.image_to_string(img))
What I want to know is does OpenCV or PyTesseract support text extraction based on font name? For example, if particular text is in Times New Roman and the rest of the text is Arial only extract the Times New Roman. Something like this:
print(pytesseract.image_to_string(img, lang='font'))
Of course no. Tesseract hardly recognizes G from 6 and OpenCV is computer vision library.
Related
I have this image and I need tesseract to read the value.
import cv2
import pytesseract
im = cv2.imread("num.png")
print(pytesseract.image_to_string(im))
It does not print anything. Am I doing something wrong since it is pretty clear that it is a 7.
Even after scaling the image up by 5x with intercubic it still would not work. This is the image now
As described here:
By default Tesseract expects a page of text when it segments an image. If you’re just seeking to OCR a small region, try a different segmentation mode, using the --psm argument.
In this case, --psm from 6 to 10 should work fine. Example:
pytesseract.image_to_string(im, config='--psm 6')
The code is correct. I think that image of 7 is not clear enough for pytesseract. You need to preprocess the image. This link might help.
I am currently using Pytesseract to extract text from images like Amazon, ebay, (e-commerce) etc to observe certain patterns. I do not want to use a web crawler since this is about recognising certain patterns from the text on such sites. The image example looks like this:
However every website looks different so template matching wouldn't help as well. Also the image background is not of the same colour.
The code gives me about 40% accuracy. But if I crop the images into smaller size, it gives me all the text correctly.
Is there a way to take in one image, crop it into multiple parts and then extract text? The preprocessing of images does not help. What I have tried is using: rescaling, removing noise, deskewing, skewing, adaptiveThreshold, grey scale,otsu, etc but I am unable to figure out what to do.
try:
from PIL import Image
except ImportError:
import Image
import pytesseract
# import pickle
def ocr_processing(filename):
"""
This function uses Pillow to open the file and Pytesseract to find string in image.
"""
text = pytesseract.image_to_data(Image.open(
filename), lang='eng', config='--psm 6')
# text = pytesseract.image_to_string(Image.open(
# filename), lang='eng', config ='--psm 11')
return text
Just for a recommendation if you have a lot of text and you want to detect it through OCR (example image is above), "Keras" is a very good option. Much much better than pytesseract or using just EAST. It was a suggestion provided in the comments section. It was able to trace 98.99% of the text correctly.
Here is the link to the Keras-ocr documentation: https://keras-ocr.readthedocs.io/en/latest/
I'm new to Tesseract and wanted to know if there were any ways to clean up photos for a simple OCR program to get better results. Thanks in advance for any help!
The code I am using:
#loads tesseract
tess.pytesseract.tesseract_cmd =
#filepath
file_path =
image = Image.open(file_path)
#processes image
text = tess.image_to_string(image, config='')
print(text)
I've used pytesseract in the past and with the following four modifications, I could read almost anything as long as the text font wasn't too small to begin with. pytesseract seems to struggle with small writing, even after resizing.
- Convert to Black & White -
Converting the images to black and white would frequently improve the recognition of the program. I used OpenCV to do so and got the code from the end of this article.
- Crop -
If all your photos are in similar format, as in the text you need is always in the same spot, I'd recommend cropping your pictures. If possible, pass only the exact part of the photo to pytesseract that you want analyzed, the less the program has to analyze, the better. In my case, I was taking screenshots and would specify the exact region of where to take one.
- Resize -
Another thing you can do is to play with the scaling of the original photo. Sometimes after resizing to almost double it's initial size pytesseract could read the text a lot easier. Generaly, bigger text is better but there's a limit as the photo can become too pixelated after resizing to be recognizable.
- Config -
I've noticed that pytesseract can recognize text a lot easier than numbers. If possible, break the photo down into sections and whenever you have a trouble spot with numbers you can use:
pytesseract.image_to_string(image, config='digits')
I'm looking for a simple solution that would return a boolean if ANY kind of English text is present in an image file. I wish to use this to detect memes. For example, the following file should be detected as an image with text.
I've come across elaborate machine learning techniques using OpenCV but I haven't been able to fully implement it. Is there any quicker, simpler, and just as effective solution for this?
I look forward to your valuable feedback!
There is indeed simple way with opencv and pytessaract after installing you will only need to use a few lines in order to get the text
pip install opencv-python
pip install pytesseract
import cv2
import pytesseract
img = cv2.imread('yourimage.jpeg')
text = pytesseract.image_to_string(img)
Read Text from Image with One Line of Python Code
Also if you don't like the first way you can use Google vision, keep in mind it will return Json and you will extract what you need.
https://cloud.google.com/vision/docs/ocr
Python Client for Google Cloud Vision
We can use pytesseract python package for get text form the images. You can easily install like pip install pytesseract
Here is the example code:
import cv2
import pytesseract
image = cv2.imread('test.jpeg')
text = pytesseract.image_to_string(image)
print(text)
Here is my sample image
So, the output should be like
IS BITCOIN
GOING TO
$20.000
BY CHRISTMAS?
You can use OpenCV and pytesseract to perform your task.
import cv2
import pytesseract
img = cv2.imread('YOUR_IMAGE_PATH')
text = pytesseract.image_to_string(img)
print(text)
I have been using Pytesseract to extract text from image. I am currently in a restoration task of an image document. Aside from extracting text from an image, I also wanted to identify each words font, font size, whether the character is capital or not, italicized or not, bold or not and so and so forth. Is this currently possible with Tesseract? I have read the documentation of Pytesseract, but found none about it. If this is not possible, how can I make it happen? Is there any open source font recognition API's? Thanks.