How Can We Improve OpenCV's Accuracy in Detecting Paper Sheets and Filtering Out Noise?

Can OpenCV Accurately Detect Paper Sheets? Filtering Output for Clearer Results

Square detection using OpenCV can be a useful tool for image processing applications. However, when dealing with sheets of paper, it's crucial to filter the output to obtain more precise results.

In the original implementation discussed, while square detection works successfully, the output can appear cluttered. To address this, let's explore the provided code and discuss potential improvements.

The Original Code

In the code provided, the function findSquaresInImage detects squares within an input image. It employs Canny edge detection with varying threshold levels and finds contours in the resulting binary images. Contours that resemble squares (with four sides and a convex shape) are further processed to check their angles and ensure their square-like qualities.

Filtering the Output

Despite these measures, the output can still contain noise or extraneous contours. To improve the accuracy of square detection and filter out unwanted results, consider the following steps:

1. Area Thresholding:

An appropriate area threshold can help eliminate small contours that are unlikely to represent paper sheets. By setting a specific area threshold, you can exclude objects below a certain size from the detected squares.

2. Aspect Ratio Filtering:

Paper sheets typically have a rectangular aspect ratio. By calculating the aspect ratio of each detected square and excluding those with ratios that deviate significantly from a rectangular shape, you can reduce false positive results.

3. Convexity Filtering:

Ensure the detected squares are convex. Concave contours or squares with dents can be eliminated based on their convexity level.

4. Perimeter Thresholding:

Consider applying a perimeter threshold. This can help identify squares with a sufficiently large perimeter, a more typical characteristic of paper sheets.

Enhanced Sheet Detection

By incorporating these filtering techniques, the accuracy of paper sheet detection can be greatly improved. Additionally, to identify the largest square in the image, which is most likely to represent the sheet of paper, a function can be introduced to calculate the area of each square and select the one with the largest area.

Conclusion

With the implementation of these filtering techniques, you can refine the output of the square detection algorithm to effectively detect paper sheets. This enables more accurate image processing applications, such as document scanning or perspective transformation for skew reduction.

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