Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction
In-context learning using Large Language Models (LLMs) offers a compelling path to training-free post-OCR correction, yet its effectiveness for Devanagari script remains entirely unexplored. We present the first systematic evaluation of LLMs (3B-32B) for post-OCR correction in Hindi and Marathi, comparing three in-context example retrieval strategies: domain-random selection, dense semantic retrieval, and our proposed CharBM25, which retrieves examples by character n-gram BM25 similarity over OCR inputs to target shared error patterns with the test sentence. Across a 20,000-sentence benchmark spanning five news domains, retrieval strategy is the decisive factor in correction quality: Char...
| Authors | Abhishek Bhandari, Gaurav Harit |
|---|---|
| Primary category | cs.CL |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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