The provided image has various elements of a monitor. I want to extract the text of each of them. I have tried various approaches, but there is a lot of text that I cannot identify.enter image description here

1. using easyocr
2. using tesseract
3. or, the image has squares of different shapes. I have also tried to identify the rectangles and then extract the image from the identified rectangles.

• Sharpen the edges of a square. Load an image, convert it to grayscale, smooth the middle blur, and sharpen to enhance the edges.
• Obtain a binary image and remove noise. Set the threshold to get a black and white binary image. Depending on the image, either Otsu’s thresholding or adaptive thresholding will work. Here we create a rectangular kernel and perform a morphological transformation to remove noise and enhance square contours
• Detect and extract the rectangle. Next, we find the contours and filter them using a min/max threshold region. All contours that pass through the filter will be squares, so we get the bounding rectangle coordinates to extract each ROI, create a green rectangular box in the image, and use the final image low
  enter image description here
  enter image description here

Below is the logic used

import cv2
import numpy as np
from PIL import Image

def integrated_preprocess_image_with_size_filter(input_file_path, output_file_path):
    
    img = Image.open(input_file_path)
    
    
    if img.mode == 'RGBA':
        img = img.convert('RGB')

    
    dpi = img.info['dpi'] if 'dpi' in img.info else (72, 72)

    
    if dpi[0] < 300 or dpi[1] < 300:
        img.save(input_file_path, dpi=(300, 300))
        img = Image.open(input_file_path)
    
    
    img_cv = np.array(img)
    img_cv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2BGR)

    
    gray_image = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)

    
    blur_kernel_size = 3 if np.var(gray_image) < 100 else 3

    
    blurred_image = cv2.bilateralFilter(gray_image, blur_kernel_size, 75, 75)

    
    sharpening_kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
    sharpened_image = cv2.filter2D(blurred_image, -1, sharpening_kernel)

    
    _, binary_image = cv2.threshold(sharpened_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    
    kernel_size = int(np.mean(gray_image) // 50000) + 1
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
    opened_image = cv2.morphologyEx(binary_image, cv2.MORPH_OPEN, kernel)

    
    contours, _ = cv2.findContours(opened_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    for cnt in contours:
        epsilon = 0.02 * cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, epsilon, True)

        if len(approx) == 4 and 100 < cv2.contourArea(cnt) < 50000:
            x, y, w, h = cv2.boundingRect(approx)
            
            if w > 90 and h > 90:
                cv2.rectangle(img_cv, (x, y), (x+w, y+h), (0, 255, 0), 2)

    
    cv2.imwrite(output_file_path, img_cv)


integrated_preprocess_image_with_size_filter('./test_img.png', './path_to_output.jpg')

To approach it in different ways, my approach was to extract the rectangular boxes and then proceed with the OCR.

I want to know how to extract text from that image in the best way possible.

Thank you.