Borderless tables detection with deep learning and OpenCV

Adrian Rosebrock, a known CV researcher, states in his "Gentle guide to deep learning object detection" that: "object detection, regardless of whether performed via deep learning or other computer vision techniques, builds on image classification and seeks to localize precisely an area where an object appears". One approach to build a custom object detector, as he suggests, is to choose any classifier and precede it with an algorithm to select and provide regions of an image that may contain an object. Within this method, you are free to decide whether to use a traditional ML algorithm for image classification (utilising or not CNN as a feature extractor) or train a simple neural network to handle arbitrary large datasets. Despite its proven efficiency, this two-stage object detection paradigm, known as R-CNN, still relies on heavy computations and is not suitable for real-time application. It is further said in the abovementioned post that "another approach is to treat a pre-trained classification network as a base (backbone) network in a multi-component deep learning object detection framework (such as Faster R-CNN, SSD, or YOLO)".