diff --git a/document-scanner.ipynb b/document-scanner.ipynb
index 856685a..c8638b4 100644
--- a/document-scanner.ipynb
+++ b/document-scanner.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1031,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1032,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1033,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1034,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1035,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,14 +86,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1036,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "def sort_contours(elem):\n",
     "    return cv2.arcLength(elem, closed=True)\n",
     "    \n",
-    "def get_quadrilateral(grayscale: cv2.Mat, output: cv2.Mat):\n",
+    "def get_corners(grayscale: cv2.Mat, output: cv2.Mat):\n",
     "  convex_hull_mask = np.zeros((grayscale.shape[0], grayscale.shape[1], 3), dtype=np.uint8)\n",
     "\n",
     "\n",
@@ -157,7 +157,7 @@
     "    print(\"corners: \", corners)\n",
     "\n",
     "\n",
-    "  cv2.imshow('Lines', output)\n",
+    "  cv2.imshow('Corners', output)\n",
     "  cv2.waitKey(10)\n",
     "\n",
     "  \n",
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1037,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -188,78 +188,7 @@
     "cv2.imshow('Canny', edges)\n",
     "cv2.waitKey(10)\n",
     "\n",
-    "corners = get_quadrilateral(edges, input_img)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cnts,_ = cv2.findContours(edged_img.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE) # find all the contours\n",
-    "print(cnts)\n",
-    "\n",
-    "# cv2.drawContours(img, cnts, -1, (0, 255, 0), 3)\n",
-    "  \n",
-    "# cv2.imshow('Contours', img)\n",
-    "# cv2.waitKey(5000)\n",
-    "\n",
-    "# Sorting the contours in descending order based on their contour area and just taking the first 5\n",
-    "cnts = sorted(cnts,key=cv2.contourArea,reverse=True)\n",
-    "\n",
-    "c = cnts[0]\n",
-    "peri = cv2.arcLength(c,True)\n",
-    "approx = cv2.approxPolyDP(c,0.03*peri,True)\n",
-    "x, y, w, h = cv2.boundingRect(c)\n",
-    "print(x,y,w,h)\n",
-    "to_show = img.copy()\n",
-    "# draw a green rectangle to visualize the bounding rect\n",
-    "cv2.rectangle(to_show, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
-    "plt.imshow(to_show)\n",
-    "plt.title(\"contour\")\n",
-    "plt.show()\n",
-    "\n",
-    "# Traversing in contours and finding the contour with 4 sides using cv2.approxPolyDP().\n",
-    "# for c in cnts:\n",
-    "#     peri = cv2.arcLength(c,True)\n",
-    "#     approx = cv2.approxPolyDP(c,0.096*peri,True)\n",
-    "#     x, y, w, h = cv2.boundingRect(c)\n",
-    "#     # draw a green rectangle to visualize the bounding rect\n",
-    "#     cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
-    "#     plt.imshow(img)\n",
-    "#     plt.title(\"soemthing\")\n",
-    "#     plt.show()\n",
-    "#     print(len(approx), cv2.contourArea(c))\n",
-    "#     if len(approx)==4:\n",
-    "#         doc = approx\n",
-    "#         break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(cnts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# draw points/circles around the corners of the document. \n",
-    "p=[]\n",
-    "for d in doc:\n",
-    "    tuple_point = tuple(d[0])\n",
-    "    cv2.circle(img,tuple_point,3,(0,0,255),4)\n",
-    "    p.append(tuple_point)\n",
-    "    \n",
-    "cv2.imshow('Corner points detected',img)\n",
-    "cv2.waitKey(0)"
+    "corners = get_corners(edges, input_img)\n"
    ]
   },
   {