2022/01/28 by Mihaela Găman, Mihaela Gaman, Gaman, Mihaela +6
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Vehicle License Plate Recognition #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2201.12216
Accepted at ECCV Workshop on Text in Everything (TiE 2022)
openalex publication_date 2022/01/28 · arxiv created 2022/08/15 · arxiv updated 2022/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 We demonstrate that our self-paced learning strategy brings significant performance gains on two data sets of historical documents, improving the average precision of YOLOv4 with more than 12% on one data set and 39% on the other.