Abstract
Achieving robustness in recognition systems across diverse domains is crucial for their practical utility. While ample data availability is usually assumed, low-resource languages, such as ancient manuscripts and non-western languages, tend to be kept out of the equations of massive pretraining and foundational techniques due to an under representation. In this work, we aim for building models which can generalize to new distributions of data, such as alphabets, faster than centralized fine-tune strategies. For doing so, we take advantage of the recent advancements in model editing to enhance the incorporation of unseen scripts (low-resource learning). In contrast to state-of-the-art meta-learning, we showcase the effectiveness of domain merging in sparse distributions of data, with agnosticity of its relation to the overall distribution or any other prototyping necessity. Even when using the same exact training data, our experiments showcase significant performance boosts in \textbf{transfer learning} to new alphabets and \textbf{out-of-domain evaluation} in challenging domain shifts, including historical ciphered texts and non-Latin scripts. This research contributes a novel approach into building models that can easily adopt under-represented alphabets and, therefore, enable document recognition to a wider set of contexts and cultures.
Abstract (translated)
在不同领域的识别系统中实现鲁棒性对于其实用性至关重要。虽然通常假设数据充足，但像古代手稿和非西方语言这样的低资源语言由于代表性不足，在大规模预训练和基础技术中的应用较少。本研究旨在构建能够在新数据分布（如字母表）上更快泛化的模型，其速度超过集中式微调策略。为此，我们利用最近的模型编辑进展来增强未见过脚本（低资源学习）的融入。 与当前最先进的元学习方法不同，我们的工作展示了领域合并在稀疏数据分布中的有效性，并且对总体分布或任何原型需求无关联性假设。即使使用相同的训练数据，在迁移学习到新字母表以及面对具有挑战性的域转变（包括历史加密文本和非拉丁文脚本）的域外评估中，实验结果也显示了显著的性能提升。 这项研究贡献了一种新颖的方法来构建能够轻松适应代表性不足的语言文字的模型，并因此使文档识别能够在更广泛的背景和文化情境下得以应用。
URL
https://arxiv.org/abs/2506.06761
