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

低资源场景中的手写文本识别，例如具有稀有字母的手稿，是一个具有挑战性的问题。在本文中，我们提出了一种基于少量学习的手写识别方法，该方法通过只需要每个字母符号的少量图像来显着减少人工注释过程。该方法包括检测文本行图像中给定字母表的所有符号，并将获得的相似度分数解码为转录符号的最终序列。我们的模型首先在从字母表生成的合成线图像上进行预训练，该字母表可能与目标域的字母表不同。然后应用第二个训练步骤来减少源数据和目标数据之间的差距。由于这种重新训练需要注释数千个手写符号及其边界框，我们建议通过一种无监督的渐进式学习方法来避免这种人为的努力，该方法会自动为未标记的数据分配伪标签。对不同数据集的评估表明，我们的模型可以在显着减少人力的情况下产生具有竞争力的结果。该代码将在以下存储库中公开提供：https://github.com/dali92002/HTRbyMatching