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significant performance gains on two data sets of historical documents,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.

该研究提出了一种自学习算法来提高光学字符识别系统中的文本行检测性能，利用自学习策略逐渐添加缺失标注的训练例子，通过将真实的边界框与伪边界框进行非极大值抑制的组合，在历史文档的两个数据集上显著提高了 YOLOv4 的平均精度。