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.

翻译：手写文本识别与机器翻译仍面临重大挑战，尤其对于马拉地语等低资源语言而言，这些语言不仅缺乏大规模数字化语料库，其手写风格亦存在高度变异性。传统解决方案通常采用两阶段流程：首先通过OCR系统从手写图像中提取文本，随后利用机器翻译模型将文本转换为目标语言。本研究系统性地比较了传统OCR-MT流程与视觉大语言模型的性能表现，后者旨在整合两个阶段，以端到端方式直接实现手写文本图像的翻译。本研究的现实驱动力源于印度地区法院和高等法院对可扩展、高精度翻译系统的迫切需求，该系统需能有效处理诸如FIR报告、起诉书和证人陈述等法律文书的数字化工作。我们在精心构建的马拉地语手写法律文档数据集上对两种方法进行评估，目标在于实现高效的法律文档处理，即使在低资源环境下也能稳定运行。研究结果为构建鲁棒性强、可边缘部署的解决方案提供了实践指导，有助于提升非母语使用者与法律从业者对法律信息的可及性。