  An important preliminary step of optical character recognition systems is the
detection of text rows. To address this task in the context of historical data
with missing labels, we propose a self-paced learning algorithm capable of
improving the row detection performance. We conjecture that pages with more
ground-truth bounding boxes are less likely to have missing annotations. Based
on this hypothesis, we sort the training examples in descending order with
respect to the number of ground-truth bounding boxes, and organize them into k
batches. Using our self-paced learning method, we train a row detector over k
iterations, progressively adding batches with less ground-truth annotations. At
each iteration, we combine the ground-truth bounding boxes with pseudo-bounding
boxes (bounding boxes predicted by the model itself) using non-maximum
suppression, and we include the resulting annotations at the next training
iteration. We demonstrate that our self-paced learning strategy brings
significant performance gains on two data sets of historical documents,
improving the average precision of YOLOv4 with more than 12% on one data set
and 39% on the other.
