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🫧 Bubble Sheet

How the bubble sheet corrector works

1. Extract the paper from the image.

2. Convert the RGB image to gray scale image

3. Convert the image to binary image using local thresholding

4. Extract the ID box from the paper and erase it

5. Erode the image to get rid of false chosen bubbles

6. Detect the external contours an draw them on the original image

7. Calculate the bounding rectangle of the contours and get its coordinates, width and height and calculate aspect ratio

8. Calculate the contour perimeter

9. Approximate each contour to polygon

10. Calculate each contour area

11. Filter the contours so if the aspect ratio ∈ [0.8,1.2], the number of contour vertices is greater than 4, the contour area is greater than 30 (threshold to ignore small circles or contours) and the contour is a closed one then it will be considered as a bubble

12. Collect all contours areas in array and calculate the average area (the mean of areas should be very close to the bubbles area because the most frequent contours are the bubbles contours).

13. Another filter to the output contours that resulted from the first filter by check if the area of each contour is within 30% error with the average contour area then bubbles contours are only residual contours

14. Sort the contours from top to bottom

15. Calculate the number of choices and the number of question in each row by using x and y coordinates of each bubble (bubbles belong to the same question are colored with the same color).

16. Iterate over each row and sort the contours from left to right.

17. Iterate over each question and calculate the number of pixels in each bubble that equal to one.

18. Check if the student select more than one choice or no choice the answer will be X but if the student select only one choice so the answer will be the character of the choice (A, B, C, etc.).

19. Calculate the number of rows and map the result array to the real data

20. Output will be in excel sheet like this

📖 Grades Sheet

How the Grades Sheet is processed

1. Paper Extraction and Image Warping

2. Table and Cells Detection

3. ID Detection

4. Handwritten Digits Detection

5. Signs Detection

6. Output will be in excel sheet like this

Warping & Paper Extraction

1. Applied Canny Edge Detector followed by Dilation to connect broken edges

2. Extracted Contours, then extracted the largest based on area (the paper contour)

3. Measured width and height of the contour (using the 4 corners)

4. Calculated coordinates for the new points (warped), and transformed the original points to these coordinates.

Digits Detection

The main idea is to train different models then use a polling system to collectively increase the accuracy of the system — beyond the accuracy of each on their own.

Single Digits Detection

1. Not much Pre-Processing was needed for the single handwritten digits, just a noise reduction filter.

2. Computed HoG of each digit picture and passed to the classifiers to determine the output.

ID Detection

1. Extracted Contours [For Digits], Then Filtered out very small contours [Not Digits]

2. Get the Average Width of all contours

3. Divide the each contour width by the average width [integer Division]; to know how many digits are in each picture

4. If the quotient of the division is less than a certain threshold, then it contains a single digit. Otherwise, divide the contour into a number of smaller contours [each containing a single digit] according to the quotient.

5. Computed HoG of each digit picture and passed to the classifiers to determine the output.

👑 Contributing

Contributors

🔒 License