Abstract
Khmer is a low-resource language characterized by a complex script, presenting significant challenges for optical character recognition (OCR). While document printed text recognition has advanced because of available datasets, performance on other modalities, such as handwritten and scene text, remains limited by data scarcity. Training modality-specific models for each modality does not allow cross-modality transfer learning, from which modalities with limited data could otherwise benefit. Moreover, deploying many modality-specific models results in significant memory overhead and requires error-prone routing each input image to the appropriate model. On the other hand, simply training on a combined dataset with a non-uniform data distribution across different modalities often leads to degraded performance on underrepresented modalities. To address these, we propose a universal Khmer text recognition (UKTR) framework capable of handling diverse text modalities. Central to our method is a novel modality-aware adaptive feature selection (MAFS) technique designed to adapt visual features according to a particular input image modality and enhance recognition robustness across modalities. Extensive experiments demonstrate that our model achieves state-of-the-art (SoTA) performance. Furthermore, we introduce the first comprehensive benchmark for universal Khmer text recognition, which we release to the community to facilitate future research. Our datasets and models can be accessible via this gated repository\footnote{in review}.
Abstract (translated)
柬埔寨语是一种低资源语言，其特点是拥有复杂的书写系统，这给光学字符识别（OCR）带来了巨大的挑战。尽管由于可用的数据集，印刷文档文本的识别取得了进展，但在其他模态下（如手写和场景文字），性能仍然受限于数据稀缺性。为每种模态训练特定模型不允许跨模态迁移学习，而那些数据较少的模态本可以从这种迁移学习中受益。此外，部署多种特定于模态的模型会导致显著的内存开销，并需要将每个输入图像路由到适当模型的过程（该过程容易出错）。另一方面，在具有不同模态间非均匀数据分布的组合数据集上简单地进行训练往往导致在代表性不足的模态下性能下降。为了解决这些问题，我们提出了一种通用柬埔寨文本识别（UKTR）框架，能够处理各种文本模态。我们的方法的核心是一种新颖的认知模态自适应特征选择（MAFS）技术，旨在根据特定输入图像的模态调整视觉特征，并增强跨模态识别的稳健性。广泛的实验表明，我们的模型达到了最先进的性能。此外，我们还引入了第一个针对通用柬埔寨文本识别的全面基准测试，我们将此发布到社区以促进未来的研究。我们的数据集和模型可以通过这个受控访问仓库获取（注：正在审查中）。
URL
https://arxiv.org/abs/2603.00702
