CourtNav: Voice-Guided, Anchor-Accurate Navigation of Long Legal Documents in Courtrooms

Sai Khadloya, Kush Juvekar, Arghya Bhattacharya, Utkarsh Saxena


Abstract
Judicial work depends on close reading of longrecords, charge sheets, pleadings, annexures,orders, often spanning hundreds of pages. Withlimited staff support, exhaustive reading duringhearings is impractical. We present CourtNav,a voice-guided, anchor-first navigator for legalPDFs that maps a judge’s spoken command(e.g., “go to paragraph 23”, “highlight the contradiction in the cross-examination”) directlyto a highlighted paragraph in seconds. CourtNav transcribes the command, classifies intentwith a grammar-first, LLM-backed router, retrieves over a layout-aware hybrid index, andauto-scrolls the viewer to the cited span whilehighlighting it and close alternates. By design, the interface shows only grounded pas-sages, never free text, keeping evidence verifiable and auditable. This need is acute in India, where judgments and cross-examinations notoriously long.In a pilot on representative charge sheets, pleadings, and orders, median time-to-relevance drops from 3–5 minutes (manual navigation) to 10–15 seconds;with quick visual verification included, 30–45seconds. Under fixed time budgets, thisnavigation-first design increases the breadth ofthe record actually consulted while preservingcontrol and transparency
Anthology ID:
2025.nllp-1.25
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2025
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Nikolaos Aletras, Ilias Chalkidis, Leslie Barrett, Cătălina Goanță, Daniel Preoțiuc-Pietro, Gerasimos Spanakis
Venues:
NLLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
351–358
Language:
URL:
https://preview.aclanthology.org/ingest-emnlp/2025.nllp-1.25/
DOI:
Bibkey:
Cite (ACL):
Sai Khadloya, Kush Juvekar, Arghya Bhattacharya, and Utkarsh Saxena. 2025. CourtNav: Voice-Guided, Anchor-Accurate Navigation of Long Legal Documents in Courtrooms. In Proceedings of the Natural Legal Language Processing Workshop 2025, pages 351–358, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
CourtNav: Voice-Guided, Anchor-Accurate Navigation of Long Legal Documents in Courtrooms (Khadloya et al., NLLP 2025)
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PDF:
https://preview.aclanthology.org/ingest-emnlp/2025.nllp-1.25.pdf