How can OpenCV be used to address convexity defects in Sudoku squares during digit extraction?

Resolving Convexity Defects in Sudoku Squares using OpenCV

Problem:

In the process of extracting digits from a Sudoku image using OpenCV, a discrepancy arises between the true boundary of the Sudoku (red line) and the approximated contour (green line) after image warping. This displacement may affect the accuracy of the OCR process.

Solution:

1. Image Adjustment:

To compensate for variations in brightness, divide each pixel by the result of a closing operation to adjust the image's contrast.

2. Identifying the Sudoku Area:

Perform connected component analysis to extract the component with the largest convex area. This represents the Sudoku grid.

3. Masking the Grid:

Create a mask by filling the component identified in step 2. This mask will be used to exclude the background from subsequent operations.

4. Detecting Grid Lines:

Apply a 2nd order derivative filter to the image to detect vertical and horizontal lines in separate images.

5. Extracting Grid Lines:

Use connected component analysis again to extract individual grid lines. Based on their caliper length, select only those lines that represent the Sudoku grid.

6. Intersection Points:

For each pair of vertical and horizontal grid lines, dilate and intersect them. Calculate the center of the result to get the intersection points of the grid lines.

7. Interpolation Functions:

Define interpolation functions for X/Y mapping using these intersection points.

8. Image Transformation:

Transform the original image using the interpolation functions to obtain a warped image with the correct Sudoku boundaries.

Implementation in OpenCV:

This solution requires translating the provided Mathematica code into OpenCV equivalents. The specific OpenCV functions and parameters to use may vary depending on the version and image characteristics.

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