The background of the image is the problem. You can omit by selecting a height-range

For example: If you select the height-range between: h/4 - (3*h)/4, result will be: (image is resized due to exceeding 2MiB.)

When you read:

Nikhil Suthar
Regional Coordinator - OSD

Email | Mob. |

Govt. Technical High School Campus, Near Aurobindo
Ashram Dandia Bazar, Vadodara - 390001, Gujarat, India
www.gtu.ac.in | www.gtuinnovationcouncil.ac.in

Code:

import cv2
from pytesseract import image_to_string

img = cv2.imread("Oa9svHu.jpeg")
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
(h, w) = gry.shape[:2]
gry = gry[int(h/4):int((3*h)/4), 0:w]
txt = image_to_string(gry)
print(txt.strip())
cv2.imshow("gry", gry)
cv2.waitKey(0)

Here's a simple approach

• Convert image to grayscale
• Otsu's threshold
• Dilate to connect contours
• Find contours and extract ROI for each word
• Perform OCR and remove word

After converting to grayscale, we Otsu's threshold to obtain a binary image

Next we invert the image and dilate to form a single contour for each word

From here we find contours and extract the ROI for each word. Here's the detected ROIs

We throw each ROI into Pytesseract OCR. If the OCR result is a word we want to remove, we simply "delete" the word by filling in the ROI with white and replace it in the original image

With

words_to_remove = ['on', 'you', 'crazy']

The result is

Similarly with

words_to_remove = ['on', 'you', 'shine', 'diamond']

The result is

Finally with

words_to_remove = ['on', 'you', 'crazy', 'diamond']

import cv2
import pytesseract

words_to_remove = ['on', 'you', 'crazy', 'diamond']
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

image = cv2.imread("1.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
inverted_thresh = 255 - thresh
dilate = cv2.dilate(inverted_thresh, kernel, iterations=4)

cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    ROI = thresh[y:y+h, x:x+w]
    data = pytesseract.image_to_string(ROI, lang='eng',config='--psm 6').lower()
    if data in words_to_remove:
        image[y:y+h, x:x+w] = [255,255,255]

cv2.imshow("thresh", thresh)
cv2.imshow("dilate", dilate)
cv2.imshow("image", image)
cv2.waitKey(0)

The cv2 pre-processing is unecessary here, tesseract is able to find the text on its own. See the example below, commented inline:

results = pytesseract.image_to_data('1.png', config='--psm 11', output_type='dict')

for i in range(len(results["text"])):
    # extract the bounding box coordinates of the text region from
    # the current result
    x = results["left"][i]
    y = results["top"][i]
    w = results["width"][i]
    h = results["height"][i]
    # Extract the confidence of the text
    conf = int(results["conf"][i])
    
    if conf > 60: # adjust to your liking
        # Cover the text with a white rectangle
        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), -1)

Detected text on the left, cleaned image on the right:

Here's a simple approach using OpenCV and Pytesseract OCR. To perform OCR on an image, its important to preprocess the image. The idea is to obtain a processed image where the text to extract is in black with the background in white. To do this, we can convert to grayscale, apply a slight Gaussian blur, then Otsu's threshold to obtain a binary image. From here, we can apply morphological operations to remove noise. Finally we invert the image. We perform text extraction using the --psm 6 configuration option to assume a single uniform block of text. Take a look here for more options.

Here's a visualization of the image processing pipeline:

Input image

Convert to grayscale -> Gaussian blur -> Otsu's threshold

Notice how there are tiny specs of noise, to remove them we can perform morphological operations

Finally we invert the image

Result from Pytesseract OCR

2HHH

Code

import cv2
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.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]

# Morph open to remove noise and invert image
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
invert = 255 - opening

# Perform text extraction
data = pytesseract.image_to_string(invert, lang='eng', config='--psm 6')
print(data)

cv2.imshow('thresh', thresh)
cv2.imshow('opening', opening)
cv2.imshow('invert', invert)
cv2.waitKey()

Tesseract, while being "good" is not the best. For critical applications I tend to use EasyOCR so maybe give that a shot.

It's free and opensource as well.

Here's an example:

import easyocr
reader = easyocr.Reader(['en']) # this needs to run only once to load the model into memory



result = reader.readtext('chinese.jpg')
print(result)