Self-paced learning to improve text row detection in historical documents with missing labels. (arXiv:2201.12216v3 [cs.CV] UPDATED)

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 …

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