diff --git a/OCR/EasyOCR_2.py b/OCR/EasyOCR_2.py
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+import cv2
+from matplotlib import pyplot as plt
+import numpy as np
+import easyocr
+import imutils
+import random
+
+img = cv2.imread("Test5.jpg") #read image
+plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+plt.title('Original Image')
+plt.show()
+
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #convert image to gray
+bfilter = cv2.bilateralFilter(gray, 11, 17, 17) #Noise reduction
+plt.imshow(cv2.cvtColor(bfilter, cv2.COLOR_BGR2RGB)) #show processed image
+plt.title('Processed Image')
+plt.show()
+
+edged = cv2.Canny(bfilter, 30, 200) #Edge detection
+plt.imshow(cv2.cvtColor(edged, cv2.COLOR_BGR2RGB))
+plt.title('Edged Processed Image')
+plt.show()
+
+keypoints = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #Find contours 
+contours = imutils.grab_contours(keypoints) #Grab contours 
+contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10] #Sort contours
+
+#Loop over our contours to find the best possible approximate contour of 10 contours
+location = None
+for contour in contours:
+    approx = cv2.approxPolyDP(contour, 10, True)
+    if len(approx) == 4:
+        location = approx
+        break
+        
+print("Location: ", location)
+
+mask = np.zeros(gray.shape, np.uint8) #create blank image with same dimensions as the original image
+new_image = cv2.drawContours(mask, [location], 0,255, -1) #Draw contours on the mask image
+new_image = cv2.bitwise_and(img, img, mask=mask) #Take bitwise AND between the original image and mask image
+
+plt.imshow(cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB)) #show the final image
+plt.show()
+
+(x,y) = np.where(mask==255) #Find the co-ordinates of the four corners of the document
+(x1, y1) = (np.min(x), np.min(y)) #Find the top left corner
+(x2, y2) = (np.max(x), np.max(y)) #Find the bottom right corner
+cropped_image = gray[x1:x2+1, y1:y2+1] #Crop the image using the co-ordinates
+
+plt.imshow(cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)) #show the cropped image
+plt.show()
+
+reader = easyocr.Reader(['en']) #create an easyocr reader object with english as the language
+result = reader.readtext(cropped_image) #read text from the cropped image
+print(result)
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