diff --git a/scripts/README.md b/scripts/README.md
index d869df5..2366fbb 100644
--- a/scripts/README.md
+++ b/scripts/README.md
@@ -26,3 +26,7 @@ Description
 - tictactoe <br />
 	A cli-based tictactoe game to play with the computer. <br/>
 	[Rounak Vyas](http://www.github.com/itsron717)
+
+- handgestures <br />
+	Simple Hand Gesture Detection System to identify numbers with OpenCV. <br/>
+	[Akash Ramjyothi](https://github.com/Akash-Ramjyothi)
diff --git a/scripts/handgestures/README.md b/scripts/handgestures/README.md
new file mode 100644
index 0000000..6e45d7b
--- /dev/null
+++ b/scripts/handgestures/README.md
@@ -0,0 +1,10 @@
+# Hand Gesture Detection
+
+Simple Hand Gesture Detection System to identify numbers with OpenCV.
+
+## Sample Demo:
+
+![ezgif com-gif-maker (2)](https://user-images.githubusercontent.com/54114888/94371093-7ee5e700-0111-11eb-9c11-642847acebf6.gif)
+
+## Usage
+`python main.py`
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diff --git a/scripts/handgestures/main.py b/scripts/handgestures/main.py
new file mode 100644
index 0000000..207392c
--- /dev/null
+++ b/scripts/handgestures/main.py
@@ -0,0 +1,112 @@
+# Imports
+import numpy as np
+import cv2
+import math
+
+# Open Camera
+capture = cv2.VideoCapture(0)
+
+while capture.isOpened():
+
+    # Capture frames from the camera
+    ret, frame = capture.read()
+
+    # Get hand data from the rectangle sub window
+    cv2.rectangle(frame, (100, 100), (300, 300), (0, 255, 0), 0)
+    crop_image = frame[100:300, 100:300]
+
+    # Apply Gaussian blur
+    blur = cv2.GaussianBlur(crop_image, (3, 3), 0)
+
+    # Change color-space from BGR -> HSV
+    hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)
+
+    # Create a binary image with where white will be skin colors and rest is black
+    mask2 = cv2.inRange(hsv, np.array([2, 0, 0]), np.array([20, 255, 255]))
+
+    # Kernel for morphological transformation
+    kernel = np.ones((5, 5))
+
+    # Apply morphological transformations to filter out the background noise
+    dilation = cv2.dilate(mask2, kernel, iterations=1)
+    erosion = cv2.erode(dilation, kernel, iterations=1)
+
+    # Apply Gaussian Blur and Threshold
+    filtered = cv2.GaussianBlur(erosion, (3, 3), 0)
+    ret, thresh = cv2.threshold(filtered, 127, 255, 0)
+
+    # Show threshold image
+    cv2.imshow("Thresholded", thresh)
+
+    # Find contours
+    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+    try:
+        # Find contour with maximum area
+        contour = max(contours, key=lambda x: cv2.contourArea(x))
+
+        # Create bounding rectangle around the contour
+        x, y, w, h = cv2.boundingRect(contour)
+        cv2.rectangle(crop_image, (x, y), (x + w, y + h), (0, 0, 255), 0)
+
+        # Find convex hull
+        hull = cv2.convexHull(contour)
+
+        # Draw contour
+        drawing = np.zeros(crop_image.shape, np.uint8)
+        cv2.drawContours(drawing, [contour], -1, (0, 255, 0), 0)
+        cv2.drawContours(drawing, [hull], -1, (0, 0, 255), 0)
+
+        # Find convexity defects
+        hull = cv2.convexHull(contour, returnPoints=False)
+        defects = cv2.convexityDefects(contour, hull)
+
+        # Use cosine rule to find angle of the far point from the start and end point i.e. the convex points (the finger
+        # tips) for all defects
+        count_defects = 0
+
+        for i in range(defects.shape[0]):
+            s, e, f, d = defects[i, 0]
+            start = tuple(contour[s][0])
+            end = tuple(contour[e][0])
+            far = tuple(contour[f][0])
+
+            a = math.sqrt((end[0] - start[0]) * 2 + (end[1] - start[1]) * 2)
+            b = math.sqrt((far[0] - start[0]) * 2 + (far[1] - start[1]) * 2)
+            c = math.sqrt((end[0] - far[0]) * 2 + (end[1] - far[1]) * 2)
+            angle = (math.acos((b * 2 + c * 2 - a ** 2) / (2 * b * c)) * 180) / 3.14
+
+            # if angle > 90 draw a circle at the far point
+            if angle <= 90:
+                count_defects += 1
+                cv2.circle(crop_image, far, 1, [0, 0, 255], -1)
+
+            cv2.line(crop_image, start, end, [0, 255, 0], 2)
+
+        # Print number of fingers
+        if count_defects == 0:
+            cv2.putText(frame, "ONE", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2,(0,0,255),2)
+        elif count_defects == 1:
+            cv2.putText(frame, "TWO", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2,(0,0,255), 2)
+        elif count_defects == 2:
+            cv2.putText(frame, "THREE", (5, 50), cv2.FONT_HERSHEY_SIMPLEX, 2,(0,0,255), 2)
+        elif count_defects == 3:
+            cv2.putText(frame, "FOUR", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2,(0,0,255), 2)
+        elif count_defects == 4:
+            cv2.putText(frame, "FIVE", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2,(0,0,255), 2)
+        else:
+            pass
+    except:
+        pass
+
+    # Show required images
+    cv2.imshow("Gesture", frame)
+    all_image = np.hstack((drawing, crop_image))
+    cv2.imshow('Contours', all_image)
+
+    # Close the camera if 'q' is pressed
+    if cv2.waitKey(1) == ord('q'):
+        break
+
+capture.release()
+cv2.destroyAllWindows()
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