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。

OTS：一种用于历史手稿文本定位的单次学习方法

OTS: A One-shot Learning Approach for Text Spotting in Historical  Manuscripts

Scholarly writing presents a complex space that generally follows a
methodical procedure to plan and produce both rationally sound and creative
compositions. Recent works involving large language models (LLM) demonstrate
considerable success in text generation and revision tasks; however, LLMs still
struggle to provide structural and creative feedback on the document level that
is crucial to academic writing. In this paper, we introduce a novel taxonomy
that categorizes scholarly writing behaviors according to intention, writer
actions, and the information types of the written data. We also provide
ManuScript, an original dataset annotated with a simplified version of our
taxonomy to show writer actions and the intentions behind them. Motivated by
cognitive writing theory, our taxonomy for scientific papers includes three
levels of categorization in order to trace the general writing flow and
identify the distinct writer activities embedded within each higher-level
process. ManuScript intends to provide a complete picture of the scholarly
writing process by capturing the linearity and non-linearity of writing
trajectory, such that writing assistants can provide stronger feedback and
suggestions on an end-to-end level. The collected writing trajectories are
viewed at this https URL

本研究提出了一种新的分类法，根据意图、作者行为和书面数据信息类型对学术写作行为进行分类，并提供了 ManuScript 数据集，旨在提供学术写作的完整图景，以捕捉写作轨迹的线性和非线性，使写作助手可以提供端到端的更强反馈和建议。