Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. The main difficulty comes from the very few annotated data and the limited linguistic information (e.g. dictionaries and language models). Thus, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human labor annotation process, requiring only few images of each alphabet symbol. The method consists in 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 any alphabet, even though different from the target domain. A second training step is then applied to diminish the gap between the source and 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 non-annotated data. The evaluation on different manuscript datasets show that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in this repository: \url{https://github.com/dali92002/HTRbyMatching}
翻译:在低资源情景下手写文本识别,如手写文本(如手稿和稀有字母的手稿)是一个棘手的问题。主要困难来自为数不多的附加说明的数据和有限的语言信息(例如词典和语言模型)。因此,我们建议采用几发基于学习的笔迹识别方法,大大减少人工劳动批注过程,只需要每个字母符号的几幅图象。这种方法包括在文本线图像中检测给定字母的所有符号,并解码与转录符号的最后序列的相似分数。我们的模型首先先在从任何字母中生成的合成线图象上进行预先训练,即使与目标域不同。然后采用第二个培训步骤缩小源和目标数据之间的差距。由于这种再培训需要批注数千个手写符号及其捆绑框,因此我们建议通过一种不严密的渐进学习方法避免这种人类努力,该方法自动为非附加说明的数据指定假标签。对不同的手写数据集的评估表明,我们的模型可以导致竞争性的结果,而人类努力将显著减少。这个代码将公开提供:TR9MM/HQ/M/ 。