Right, so those would be the ones that I would set to target_length=0 if that didn’t throw an error.  There is code to not include them in the batches that I’ve used so that indeed every training item has a valid label sequence (it’s commented out under # For removing all blank images).  When that code is uncommented and the blank images are excluded, it still goes to predicting just the blank label after a few epochs.
Sorry for the confusion with this!  My original thought process was that I thought that some negative examples would be good so that it didn’t predict labels when there aren’t any.  I could, of course, just train a separate CNN that makes the decision of blank or not and then if not blank then feed it to the CTC trained network for generating labels.