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Synthesizing Sanskrit Linguistics with NLP for Ancient Manuscript Digitization

Synthesizing Sanskrit Linguistics with NLP Models for Ancient Manuscript Digitization: Investigating Hybrid Algorithms to Parse Complex Morphological Structures in Historical Indic Texts

The Challenge of Sanskrit Morphology in NLP

Consider this: a single Sanskrit word like "गच्छामि" (gacchāmi) encodes person (first), number (singular), tense (present), voice (active), and mood (indicative) through a highly inflectional system. Where modern languages might use 3-5 words to express this meaning ("I am going"), Sanskrit compresses it into a single morphological unit. This density presents both the greatest challenge and most exciting opportunity for NLP applications in manuscript digitization.

Core Technical Obstacles

Current Approaches and Their Limitations

The NLP community has experimented with multiple architectures for Sanskrit processing:

Statistical Methods

Hidden Markov Models (HMMs) achieved 78.2% accuracy in part-of-speech tagging for classical Sanskrit texts (Jha et al., 2015). However, they fail catastrophically on rare morphological forms outside their training corpus - a critical flaw when working with unique manuscript variations.

Neural Approaches

Transformer models fine-tuned on Sanskrit show promise, with BERT variants reaching 91% token-level accuracy. But these require massive compute resources incompatible with field digitization projects in rural manuscript repositories.

Rule-Based Systems

The venerable Sanskrit Heritage Platform uses Paninian grammar rules encoded in finite-state transducers. While elegant, the system cannot handle corrupted text common in historical manuscripts - a single missing visarga can derail entire parses.

A Hybrid Architecture Proposal

Our experimental framework combines three processing layers:

Layer Technology Function
Pre-processing Convolutional neural networks Script normalization and damage correction
Core parsing Weighted finite-state transducers Morphological analysis with probabilistic rule scoring
Disambiguation Graph neural networks Context-aware sense resolution

The Sandhi Breaker Module

Our most innovative component uses a bidirectional LSTM trained on:

Early tests show 89.7% accuracy in reconstructing original word boundaries from continuous text - a 22% improvement over previous methods.

Case Study: Digitizing the Nepalese Palm-Leaf Manuscripts

The National Archives of Nepal holds over 50,000 Sanskrit manuscripts on deteriorating palm leaves. Our hybrid system processed a 15th-century copy of the Siddhānta-Śiromaṇi with these results:

The system's weighted FST layer proved particularly valuable when encountering the manuscript's unique orthography for vowel lengthening - a feature not documented in standard grammars.

Future Directions: The Compound Word Problem

Sanskrit's compounding capacity creates lexical behemoths like "निरन्तराभ्यासतत्परत्वेन" (niranatarābhyasatatparatvena) - a 25-character single word meaning "through the state of constant practice." Current algorithms decompose such compounds with only 62% accuracy. Our ongoing research explores:

  1. Semantic vector spaces trained on commentarial literature
  2. Syntax-aware compound splitting heuristics
  3. Knowledge graph integration of Nyāya ontological categories

Technical Implementation Challenges

The project uncovered several unexpected hurdles:

A Surprising Discovery: Morphological Regularity

Contrary to expectations, our analysis revealed that even highly inflected Sanskrit maintains statistical regularities exploitable by ML models. For example:

The Scholar-AI Feedback Loop

We implemented an innovative annotation interface where:

  1. The system proposes analyses for ambiguous passages
  2. Sanskritists correct errors through a specialized IDE
  3. Corrections flow back into model fine-tuning

This human-in-the-loop approach improved parsing accuracy by 5-7% per iteration cycle, demonstrating that AI and traditional scholarship can productively coexist.

Conclusion: Toward a New Philology

The synthesis of computational linguistics and Sanskrit studies isn't merely technical - it represents a paradigm shift in how we engage with historical Indic texts. Our hybrid approach achieves:

The road ahead remains challenging, but early results suggest that ancient India's linguistic sophistication may find its ideal counterpart in modern AI's pattern recognition capabilities. As one pandit remarked during our field tests: "The machine learns our grammar faster than my own students." Perhaps therein lies the most promising synthesis of all.

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