I'm trying to extract text from image using python cv2. The result is pathetic and I can't figure out a way to improve my code. I believe the image needs to be processed before the extraction of text but not sure how.
I've tried to convert it into black and white but no luck.
import cv2
import os
import pytesseract
from PIL import Image
import time
pytesseract.pytesseract.tesseract_cmd='C:\\Program Files\\Tesseract-OCR\\tesseract.exe'
cam = cv2.VideoCapture(1,cv2.CAP_DSHOW)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, 8000)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 6000)
while True:
return_value,image = cam.read()
image=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image = image[127:219, 508:722]
#(thresh, image) = cv2.threshold(image, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
cv2.imwrite('test.jpg',image)
print('Text detected: {}'.format(pytesseract.image_to_string(Image.open('test.jpg'))))
time.sleep(2)
cam.release()
#os.system('del test.jpg')
You can use the Microsoft OneNote app to extract text from images. Here is how: Run the OneNote app, and click Insert > Pictures > From File. Go to where you store the image, then double click to insert this image. Right click the inserted image, then select Copy Text From Picture. Right click an empty space and select Paste.
The text extractor will allow you to extract text from any image. You may upload an image or document (.pdf) and the tool will pull text from the image. Once extracted, you can copy to your clipboard with one click. Explore other Workbench solutions File Converter Tool
It is the conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).
It performs AI-based extraction of text to provide 100% accuracy. This image-to-text generator supports multiple languages. It means you can extract text in various languages such as English, Spanish, Dutch, Italian, etc. After you are done capturing text, you can download it as a text file. This file can be used to edit the text as per your needs.
Preprocessing to clean the image before performing text extraction can help. Here's a simple approach
First we convert to grayscale then sharpen the image using a sharpening kernel
Next we adaptive threshold to obtain a binary image
Now we perform morphological transformations to smooth the image
Finally we invert the image
import cv2
import numpy as np
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
sharpen_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
sharpen = cv2.filter2D(gray, -1, sharpen_kernel)
thresh = cv2.threshold(sharpen, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=1)
result = 255 - close
cv2.imshow('sharpen', sharpen)
cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('result', result)
cv2.waitKey()
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