main.py

from pdf2image import convert_from_path
import pytesseract
import cv2
from PIL import Image
from matplotlib import pyplot as plt

pdfs = r'D:/PythonWorks/ExtractDataFromPDF/ISO.pdf'
pages = convert_from_path(pdfs, 350)

""""
i = 1
for page in pages:
    image_name = "Page_" + str(i) + ".jpg"
    page.save(image_name, "JPEG")
    i = i+1
"""
imagE_path='D:/PythonWorks/ExtractDataFromPDF/Page_1.jpg'
# use this command to install open cv2
# pip install opencv-python

# use this command to install PIL
# pip install Pillow

def mark_region(image_path):
    
    im = cv2.imread(r"D:/PythonWorks/ExtractDataFromPDF/Page_1.jpg")
    if(im.size>2):
        gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (9,9), 0)
    thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,11,30)

    # Dilate to combine adjacent text contours
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
    dilate = cv2.dilate(thresh, kernel, iterations=4)

    # Find contours, highlight text areas, and extract ROIs
    cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]

    line_items_coordinates = []
    for c in cnts:
        area = cv2.contourArea(c)
        x,y,w,h = cv2.boundingRect(c)

        if y >= 2000 and x >=4000:
            if area > 10000:
                image = cv2.rectangle(im, (x,y), (2200, y+h), color=(255,0,255), thickness=3)
                line_items_coordinates.append([(x,y), (2200, y+h)])

        if y >= 2400 and x<= 2000:
            image = cv2.rectangle(im, (x,y), (2200, y+h), color=(255,0,255), thickness=3)
            line_items_coordinates.append([(x,y), (2200, y+h)])


    return image, line_items_coordinates


pytesseract.pytesseract.tesseract_cmd = r'C:/Program Files/Tesseract-OCR/tesseract.exe'

image, line_items_coordinates = mark_region(imagE_path)

# load the original image
#image = cv2.imread(imagE_path)

# get co-ordinates to crop the image
c = line_items_coordinates[1]

# cropping image img = image[y0:y1, x0:x1]
img = image[c[0][1]:c[1][1], c[0][0]:c[1][0]]    

plt.figure(figsize=(10,10))
plt.imshow(img)

# convert the image to black and white for better OCR
ret,thresh1 = cv2.threshold(img,120,255,cv2.THRESH_BINARY)

# pytesseract image to string to get results
text = str(pytesseract.image_to_string(thresh1, config='--psm 6'))
print(text)