Abstract
Handwritten text recognition (HTR) and machine translation continue to pose significant challenges, particularly for low-resource languages like Marathi, which lack large digitized corpora and exhibit high variability in handwriting styles. The conventional approach to address this involves a two-stage pipeline: an OCR system extracts text from handwritten images, which is then translated into the target language using a machine translation model. In this work, we explore and compare the performance of traditional OCR-MT pipelines with Vision Large Language Models that aim to unify these stages and directly translate handwritten text images in a single, end-to-end step. Our motivation is grounded in the urgent need for scalable, accurate translation systems to digitize legal records such as FIRs, charge sheets, and witness statements in India's district and high courts. We evaluate both approaches on a curated dataset of handwritten Marathi legal documents, with the goal of enabling efficient legal document processing, even in low-resource environments. Our findings offer actionable insights toward building robust, edge-deployable solutions that enhance access to legal information for non-native speakers and legal professionals alike.
Abstract (translated)
手写文本识别（HTR）和机器翻译在像马拉地语这样资源匮乏的语言中仍面临重大挑战，这些语言缺乏大规模数字化语料库，并且手写风格具有高度的变异性。传统方法是采用两阶段管道：光学字符识别（OCR）系统从手写图像中提取文本，然后使用机器翻译模型将该文本翻译成目标语言。在这项工作中，我们探讨并比较了传统OCR-MT管道与旨在统一这些步骤并将手写文本图像直接转换为另一种语言的视觉大规模语言模型的表现。我们的动机是基于印度地方法院和高等法院数字化法律记录（如第一信息报告、起诉书和证人陈述）对可扩展且准确翻译系统的迫切需求。 我们在精心策划的手写马拉地语法律文件数据集上评估了这两种方法，旨在实现低资源环境下的高效法律文档处理。我们的研究结果为构建稳健的边缘部署解决方案提供了可行见解，这些方案能够提高非母语者和法律专业人士获取法律信息的途径。
URL
https://arxiv.org/abs/2512.18004
