Historical manuscript processing poses challenges like limited annotated
training data and novel class emergence. To address this, we propose a novel
One-shot learning-based Text Spotting (OTS) approach that accurately and
reliably spots novel characters with just one annotated support sample. Drawing
inspiration from cognitive research, we introduce a spatial alignment module
that finds, focuses on, and learns the most discriminative spatial regions in
the query image based on one support image. Especially, since the low-resource
spotting task often faces the problem of example imbalance, we propose a novel
loss function called torus loss which can make the embedding space of distance
metric more discriminative. Our approach is highly efficient and requires only
a few training samples while exhibiting the remarkable ability to handle novel
characters, and symbols. To enhance dataset diversity, a new manuscript dataset
that contains the ancient Dongba hieroglyphics (DBH) is created. We conduct
experiments on publicly available VML-HD, TKH, NC datasets, and the new
proposed DBH dataset. The experimental results demonstrate that OTS outperforms
the state-of-the-art methods in one-shot text spotting. Overall, our proposed
method offers promising applications in the field of text spotting in
historical manuscripts.

通过一种新颖的一次学习文本定位方法，即 One-shot learning-based Text Spotting (OTS) 方法，该方法结合了认知研究中的方法，通过空间对齐模块来找到并学习查询图像中最具有区分性的空间区域。同时，引入一种名为环形损失（Torus loss）的新型损失函数，使得嵌入距离空间更具有区分性。我们自己创建了包含古代东巴象形文字的手稿数据集，并在公开数据集上进行了实验，结果表明该方法在一次性文本定位方面优于现有方法。通过 OTS 方法，有望在历史手稿文本识别方面有更好的应用。