How to Rectify Image Convexity for Accurate Sudoku Recognition?

How to Rectify Image Convexity for Sudoku Recognition

In a Sudoku solver project, users encountered an issue where the convexity of the Sudoku square caused inaccuracies in warping the image to a perfect square. The green line approximating the contour diverged from the true boundary (red line), potentially leading to incorrect identification of the Sudoku numbers.

Our solution aims to address this convexity defect by leveraging image processing techniques.

1. Image Adjustment

We first adjust the image brightness by dividing each pixel by a closing operation result, enhancing contrast and reducing the impact of background noise.

2. Sudoku Area Identification

Using connected component analysis, we identify the Sudoku area as the component with the largest convex area, excluding the background.

3. Grid Line Extraction

We apply a 2nd order derivative filter to extract the vertical and horizontal grid lines into separate images. Connected component analysis is then performed to extract the grid lines based on their length.

4. Grid Intersection Detection

For each pair of vertical and horizontal grid lines, dilation is applied to broaden their size. The intersection of the dilated images is calculated, and the center of the intersection represents the grid intersection point.

5. Image Warping

Based on the grid intersection points, we define interpolation functions for X and Y mapping. The warping operation transforms the image using these functions, rectifying the convexity and aligning it to the true Sudoku boundary.

This approach utilizes basic image processing techniques and can be easily implemented in OpenCV for improved Sudoku recognition accuracy.

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