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app.py

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+import cv2
+import cv2 as cv
+import numpy as np
+import streamlit as st
+from PIL import Image
+import pytesseract
+
+
+act =  [(150,240), (610,260)]
+
+
+def align_images(ref_gray, input_gray):
+    """
+    Aligns the input image to the reference image using homography.
+    
+    Parameters:
+        reference_image (numpy.ndarray): The reference image.
+        input_image (numpy.ndarray): The input image to be aligned.
+    
+    Returns:
+        numpy.ndarray: The aligned version of the input image.
+    """
+    # # Convert images to grayscale
+    # ref_gray = cv2.cvtColor(reference_image, cv2.COLOR_BGR2GRAY)
+    # input_gray = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)
+
+    
+    # Detect ORB keypoints and descriptors
+    orb = cv2.ORB_create(nfeatures=500)
+    keypoints1, descriptors1 = orb.detectAndCompute(ref_gray, None)
+    keypoints2, descriptors2 = orb.detectAndCompute(input_gray, None)
+    
+    # Match descriptors using BFMatcher
+    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+    matches = bf.match(descriptors1, descriptors2)
+    matches = sorted(matches, key=lambda x: x.distance)
+    
+    # Extract location of good matches
+    ref_points = np.float32([keypoints1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
+    input_points = np.float32([keypoints2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
+    
+    # Compute homography matrix
+    H, mask = cv2.findHomography(input_points, ref_points, cv2.RANSAC, 5.0)
+    
+    # Warp input image to align with reference image
+    height, width = ref_gray.shape
+    aligned_image = cv2.warpPerspective(input_gray, H, (width, height))
+    
+    return aligned_image
+
+def ocr_with_crop(aligned_image):
+    # Open the image
+    # img = Image.open(image_path)
+    # img = cv2.imread(image_path,0)
+    # img = enhance(img)
+    # st.image(img)
+    # st.write(type(img))
+    # enh = enhance(np.array(img))
+    # st.image(enh)
+    # Define the coordinates for cropping
+    crop_coordinates = act
+    
+    # Convert to rectangular bounds (x1, y1, x2, y2)
+    x1, y1 = crop_coordinates[0]
+    x2, y2 = crop_coordinates[1]
+    # Crop the image using the defined coordinates
+    # cropped_img = img.crop((x1, y1, x2, y2))
+    cropped_img = aligned_image[y1:y2,x1:x2]
+    st.image(cropped_img)
+    # Perform OCR on the cropped image
+    text = pytesseract.image_to_string(cropped_img)
+    
+    # Print the extracted text
+    st.write(text)
+
+if __name__== "__main__":
+    ref = cv.imread("r.png",0)
+    if inp:= st.file_uploader("upload your form in image format", type=['png', 'jpg']):
+        image = Image.open(inp)
+        gray_image_pil = image.convert('L')
+        image_array = np.array(gray_image_pil)
+        st.image(image_array)
+        align_image = align_images(ref,image_array)
+        ocr_with_crop(align_image)
+
+

packages.txt

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+tesseract-ocr
+pytesseract

requirements.txt

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+streamlit
+pillow
+matplotlib
+numpy
+opencv-python
+pymupdf 
+pillow
+PyPDF2 
+pdf2image 
+pytesseract