1. Is Elasticsearch an appropriate tool for this use case? Are there better alternatives?

Yes, Elasticsearch is a suitable tool for this use case because of its:

• Fuzzy search capabilities: Elasticsearch supports fuzziness, which can handle typos and minor OCR errors.
• Custom analyzers: These allow tokenizing and normalizing text for flexible matching.
• Scalability: Elasticsearch can handle large datasets and perform searches efficiently.

Alternatives:

• PostgreSQL with Trigram Search or Full-Text Search: Works well for smaller datasets but lacks Elasticsearch's scalability and advanced tokenization.
• Hybrid Approach (Elastic + Vector Search): You could integrate Elasticsearch with vector-based tools like FAISS or Pinecone for semantic matching, particularly if OCR errors significantly alter the text's structure.
• Specialized Address Validation APIs: Some APIs like Loqate or Google Maps provide address validation and corrections but may not offer full control.

2. How can I ensure high recall and precision, given that OCR errors might introduce significant deviations?

To improve both recall and precision:

1. Use Fuzzy Matching: Apply Elasticsearch's fuzziness parameter in match queries. Start with fuzziness levels like 1 or 2 (fuzziness: 2) to allow up to two character changes.
2. Custom Normalizers: Use Elasticsearch analyzers to normalize text. For example:
   • Remove spaces or punctuation inconsistencies.
   • Lowercase all tokens.
3. Token-Based Search: Split addresses into tokens (e.g., State, City, Road Name) and search across multiple fields using a bool query to increase precision.
4. Synonyms and Phonetic Matching:
   • Add synonyms for common variations (e.g., "St." vs. "Street").
   • Use phonetic plugins like Elasticsearch's phonetic analyzer for handling OCR-induced spelling variations (e.g., "Nonhyeon" vs. "Nonhyon").
5. Weighting Fields: Use a multi_match query to assign higher weights to more reliable fields (e.g., State or City) while allowing flexibility for less reliable fields like Road Name or Building Number.

3. Should I use a single combined field (tokens) or search across multiple fields with a bool query?

It’s better to search across multiple fields using a bool query. This approach:

• Improves flexibility: Allows matching partial information (e.g., if only the city and road name are accurate).
• Handles structure better: Each field (State, City, Road Name, etc.) can have its own analyzers and weights.

However, a combined tokens field can complement this setup:

• Use it as a fallback for cases where matching individual fields fails.
• Apply aggressive tokenization (e.g., removing spaces and punctuation) for robust fuzzy matching.

Recommended Setup:

1. Use a bool query for structured fields.
2. Add a secondary should clause for the combined tokens field.

4. Are there specific Elasticsearch settings or plugins that could improve accuracy?

Yes, you can leverage the following:

• Custom Analyzers:
  • Use the nori analyzer for Korean language processing.
  • Apply edge n-grams or shingles for partial matches (e.g., "Nonhy" matching "Nonhyeon").
• Synonym Filters: Define common misspellings, abbreviations, or OCR variations in a synonyms file.
• Phonetic Matching: Use the phonetic analysis plugin to handle OCR errors that result in similar-sounding words.
• Boosting Important Fields: Adjust field weights in queries using boost (e.g., prioritize State and City).
• Fuzziness: Enable fuzziness in match queries for typos, with a fuzziness value of 1–2 depending on your data's error rates.

5. What limitations should I be aware of when using Elasticsearch for approximate searches?

1. Handling High Fuzziness: Higher fuzziness values can lead to irrelevant matches, reducing precision. Carefully balance fuzziness with tokenization and normalization.
2. Tokenization Limitations: If your addresses have inconsistent formats, the standard tokenizers might not work well without customization.
3. Scale of Synonyms: Maintaining large synonyms lists can become unwieldy if there are many variations to address.
4. Inconsistent Address Formats: OCR errors may disrupt the address structure (e.g., swapping fields like City and Road Name), which requires additional preprocessing or a fallback logic.
5. Cost and Maintenance: Elasticsearch requires operational expertise to maintain clusters and manage resource scaling effectively.

please refer for more :

1. How to Use Fuzzy Searches in Elasticsearch
2. Fuzzy query