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Artificial Intelligence · Major Project
Automatic OMR Sheet Evaluation
Scans and grades bubble answer sheets with OpenCV
2-3 weeks build
Overview
Photograph or scan an OMR sheet and the system finds the page, corrects its perspective, locates the bubble grid and decides which option was marked in each row by counting filled pixels. Answers are compared with an answer key to produce a score, while blank or double-marked questions are flagged for review. A Flask portal handles batch uploads and result exports.
What makes it stand up
- 01Page detection and four-point perspective correction
- 02Adaptive thresholding that copes with uneven lighting
- 03Bubble detection and fill-ratio marking decision
- 04Flags for blank, double-marked and faint answers
- 05Batch upload portal with CSV result export
Module breakdown
- 01
Preprocessing
Greyscale, blur, edge detection and page contour.
- 02
Alignment
Warps the sheet to a flat top-down view.
- 03
Bubble reading
Finds bubbles and measures how filled each one is.
- 04
Grading
Matches answers to the key and flags ambiguous rows.
- 05
Portal
Flask upload, answer-key management and results table.
After this, you will be able to
- Explain how the perspective transform flattens a photo
- Defend the fill threshold used to decide a marked bubble
- Demo grading a sheet the examiner fills in
Technology stack
- Python
- OpenCV
- NumPy
- Flask
- SQLite
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 2-3 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support