Text detection in computer vision
Many research projects use something called "hard negative mining": instead of training on all of the positive instances (e.g., "text" or "object") and all of the negative instance (e.g., "not text" or "not object"), they train on all of the positive instances and a carefully chosen subset of the negative instances.  In particular, they omit many of the 'obvious' negative instances and try to bias towards choosing negative instances that are more difficult to handle correctly.  It's not clear to me whether this is truly necessary or beneficial with modern machine learning methods, but it is something you could try, if you don't want to use all of the negative instances.

Yes, to create negative instances, you would normally sample random patches of the image (at a random location), subject to the restriction that they not have too much overlap with the actual text/objects.

For learning more about how to recognize object vs objectness, read about "objectness" detection or saliency detection.  For learning more about detecting text, you might be interested in the stroke width transform and the segmentation step in OCR.