TY  - JOUR
AU  - Mohamed Ali Souibgui
AU  - Alicia Fornes
AU  - Yousri Kessentini
AU  - Beata Megyesi
PY  - 2022//
TI  - Few shots are all you need: A progressive learning approach for low resource handwritten text recognition
T2  - PRL
JO  - Pattern Recognition Letters
SP  - 43
EP  - 49
VL  - 160
PB  - Elsevier
N2  - Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching
UR  - http://dx.doi.org/10.1016/j.patrec.2022.06.003
N1  - DAG; 600.121; 600.162; 602.230
ID  - Mohamed Ali Souibgui2022
ER  -