Optical Mark Recognition (OMR) isn’t new—we’ve all filled out those bubble sheets for tests. But traditional OMR systems are slow, require massive hardware scanners, and cost a fortune.
I wanted to see if I could build a Real-Time OMR Scanner using just a standard webcam, Python, and OpenCV. Here’s how I did it.
🧠 The Tech Stack
- Python (The backbone)
- OpenCV (For image processing and contour detection)
- Flask (To serve the live camera feed and UI)
- SQLite (To log the scanned results instantly)
📷 The Challenge: Dealing with Live Video
The biggest hurdle with live video is that hands shake, lighting changes, and paper bends.
To solve this, I had to build a robust image processing pipeline:
- Grayscale Conversion: Simplify the image data.
- Gaussian Blur: Remove camera noise.
- Canny Edge Detection: Find the edges of the paper.
- Perspective Transformation: “Flatten” the paper digitally so the bubbles are perfectly aligned, regardless of the camera angle.
🎯 Detecting the Bubbles
Once the paper was digitally flattened, I used OpenCV contour detection to isolate the bubbles. By analyzing the pixel density inside each contour, I could determine which bubble was “filled in” (it would have significantly more dark pixels than the others).
🚀 The Result
The system can now scan a bubble sheet instantly via a live webcam feed, calculate the score, and export the data directly to a CSV or SQLite database in milliseconds.
Have you ever worked with OpenCV for real-time video processing? What was the hardest part for you? Drop a comment below!
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