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OpenCV can be used to identify both the presence of a cardboard box and any text on it by following these steps:

1. Preprocessing: Perform image processing operations such as resizing, blurring, or thresholding to improve the quality of the input image.

2. Object Detection: Use a suitable object detection algorithm like HOG, Haar Cascade, or YOLO to detect the presence of a cardboard box in the preprocessed image.

3. Text Extraction: Once the cardboard box is detected, use text detection algorithms like EAST, Tesseract OCR, or Deep Learning object detection models to extract any text present on the box.

4. Text Recognition: Use OCR (Optical Character Recognition) to recognize the text from extracted regions.

5. Visualizing Results: Finally, display the results by highlighting the detected box and any recognized text in the original image.

Note: The accuracy of object detection and text recognition greatly depends on the quality of the input image and the training of the models. Therefore, tuning the hyperparameters and training the models on suitable datasets is recommended to achieve better results.