How to generate training labels?

[zwenwang]

Hi, I read your paper and have some questions about training labels and side-refinement.
About training labels, the paper says:

For text/non-text classification, a binary label is assigned to each positive(text) or negative(non-text) anchor. It is defined by computing the IoU overlap with the GT bounding box (divided by anchor location).

My first question is how to divide the GT bounding box? I think I need to divide the GT bounding box into many fine-scale bounding box (just like detect text) and then I can label anchors. According your paper I think I should divide the GT bounding box by anchor location. For a particular image and vgg-16 net, all anchors location is fixed because of the net architecture, so I need to divide the GT bounding box by these anchors?

Next question is how to compute the o* in side-refinement. If I divide the GT bounding box by anchor location, for the equation 4 in your paper, I think the xside is the right/left side of the GT bounding box, and cxa is the anchor that divide the GT bounding box. And for each GT bounding box I only need to compute the o for left/right side anchor. Is my understanding correct?
Thank you in advance. @tianzhi0549