Using Vedalakṣaṇa texts for the validation and normalization of Vedic Corpus
Paper · 2026 · Team Svarupa
Summary
Digital Vedic texts available online, such as VedaWeb, the Vedic Heritage Portal, SanskritDocuments, Wikisource, DCS and the Vedic Scriptures portal, vary in quality. Accent (svara) marks are encoded inconsistently, OCR introduces character errors, and mantras can be missing, duplicated or wrongly segmented.
The Svarupa team is building a curated Vedic corpus to fix this. Most text came from downloadable spreadsheets on the Vedic Scriptures portal, supplemented by web scraping and OCR of printed editions. The data was then normalized for Unicode, formatting and mantra boundaries. Sanskrit scholars compared it against one authoritative edition per text, paying special attention to accents.
As a second, tradition-based check, the authors draw on Vedalakṣaṇa texts. These indigenous manuals record phonological, sandhi and accent particulars, and they help choose the correct Padapāṭha split where the continuous Saṁhitā is ambiguous. The authors also propose regrouping lakṣaṇa rules by linguistic function so they map onto computational tasks.
The available text reports no accuracy figures. The paper's value is methodological: it shows that traditional rule systems can provide verifiable checks for Vedic NLP data.
Key points
- The validated dataset covers several recensions:
- Ṛgveda Śākala: 10,552 mantras
- Sāmaveda Kauthuma: 1,875 mantras
- Taittirīya (Kṛṣṇa Yajurveda): 631 anuvāk
- Mādhyandina: 1,975 mantras
- Kāṇva: 2,086 mantras
- Atharvaveda Śaunaka: 5,987 mantras
- The reference editions include the VSM (Vaidika Saṃśodhana Maṇḍala) edition for the Ṛgveda and Shadangi (2024) for the Sāmaveda.
- The errors corrected were mistyped or OCR-garbled syllables, missing or extra words and mantras, and svara mistakes.
- An illustrative case is Ṛgveda 8.102.17, where the Āvarṇi lakṣaṇa's list of ā-initial words confirms the reading ājananta.
- Lakṣaṇa rules do not perform segmentation themselves. They only select among candidate splits, so they offer a partial, text-specific evaluation. Cross-śākhā validation is planned as future work.
Fields, topics and concepts
📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 works
Cite
Cite as Team Svarupa. 2026. Using Vedalakṣaṇa texts for the validation and normalization of Vedic Corpus. Proceedings of the 8th International Sanskrit Computational Linguistics Symposium.
@inproceedings{svarupa2026using,
title = {{Using Vedalakṣaṇa texts for the validation and normalization of Vedic Corpus}},
author = {Team Svarupa},
booktitle = {Proceedings of the 8th International Sanskrit Computational Linguistics Symposium},
year = {2026},
url = {https://aclanthology.org/2026.iscls-1.12/}
}References
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