Seeing Justice Clearly: Handwritten Legal Document Translation with OCR and Vision-Language Models
This addresses the need for scalable translation systems to digitize legal documents in India's courts, though it appears incremental as it compares existing methods rather than introducing a fundamentally new paradigm.
The paper tackled the problem of translating handwritten legal documents in low-resource languages like Marathi by comparing traditional OCR-MT pipelines with Vision Large Language Models for end-to-end translation, finding that the unified approach offers more efficient processing for legal records.
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.