OCR OMR Sheet using Python on Stackoverflow

As a programmer, you may have come across the task of processing OMR sheets. This involves reading the marked bubbles on the OMR sheet and converting them into a digital format for further processing. In this article, we will explore how to use Python to perform OCR on OMR sheets.

What is an OMR sheet?

OMR (Optical Mark Recognition) sheet is a specially designed paper with bubbles or checkboxes that are marked by the user with a pencil or a pen. These sheets are then processed by a machine that reads and interprets the markings to generate a digital output.

Why use Python for OMR sheet processing?

Python is a popular programming language that is widely used for image processing and computer vision. It has a large number of libraries and packages, including OpenCV, Pillow, and PyTesseract, that make it easy to perform OCR on images.

Steps to perform OCR on OMR sheet using Python

1. Import the necessary libraries and packages. For this task, we will be using OpenCV, Pillow, and PyTesseract.

import cv2
from PIL import Image
import pytesseract

2. Load the image of the OMR sheet. You can use the cv2.imread() function to load the image in the BGR format.

img = cv2.imread('omr_sheet.png')

3. Convert the image to grayscale. This can be done using the cv2.cvtColor() function.

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

4. Threshold the image to convert the grayscale image to a binary image. This can be done using the cv2.threshold() function.

_, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)

5. Apply morphological operations to the binary image. This can be done using the cv2.erode() and cv2.dilate() functions.

kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
thresh = cv2.erode(thresh, kernel, iterations=1)
thresh = cv2.dilate(thresh, kernel, iterations=1)

6. Invert the binary image using the cv2.bitwise_not() function.

thresh = cv2.bitwise_not(thresh)

7. Convert the image to a PIL Image object using the Image.fromarray() function.

pil_img = Image.fromarray(thresh)

8. Use PyTesseract to extract the text from the image. This can be done using the pytesseract.image_to_string() function.

text = pytesseract.image_to_string(pil_img, lang='eng')

Conclusion

In this article, we explored how to use Python to perform OCR on OMR sheets. Python has a large number of libraries and packages that make image processing and computer vision tasks easy and efficient. With the steps outlined above, you should be able to extract the text from an OMR sheet and convert it into a digital format for further processing.

If you have any questions or comments, feel free to leave a comment below.