AI Use Cases for OCR and Computer Vision Integration with Azure

 

The above diagram illustrates the process of passing the unstructured JFK Files dataset through the Azure Cognitive Search skills pipeline to produce structured, indexable data:

1. Unstructured data in Azure Blob Storage, such as documents and images, ingest into Azure Cognitive Search.
2. The document cracking step initiates the indexing process by extracting images and text from the data, followed by content enrichment. The enrichment steps that occur in this process depend on the data and type of skills selected.
3. Built-in skills based on the Computer Vision and Language Service APIs enable AI enrichments including image optical character recognition (OCR), image analysis, text translation, entity recognition, and full-text search.
4. Custom skills support scenarios that require more complex AI models or services. Examples include Forms Recognizer, Azure Machine Learning models, and Azure Functions.
5. Following the enrichment process, the indexer saves the outputs into a search index that contains the enriched and indexed documents. Full-text search and other query forms can use this index.
6. The enriched documents can also project into a knowledge store, which downstream apps like knowledge mining or data science can use.
7. Queries access the enriched content in the search index. The index supports custom analyzers, fuzzy search queries, filters, and a scoring profile to tune search relevance.
8. Output is stored in Blob storage and Azure table.
9. Any application that connects to Blob Storage or to Azure Table Storage can access the knowledge store.

Potential usecases:

1. Document Search and Retrieval:

   1. Use Case: Enhance search capabilities in content management systems or data repositories.
   2. Implementation: Implement OCR to convert scanned documents into searchable text, improving document retrieval and making unstructured data more accessible.
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2. Data Enrichment for Search and Data Science:

   1. Use Case: Increase the value of unstructured text and image content in search and data science applications.
   2. Implementation: Extract key information using OCR to enrich datasets, enabling more robust analysis and insights for data science applications.
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3. Custom Skills Integration:

   1. Use Case: Tailor search functionalities by integrating custom skills.
   2. Implementation: Employ Azure Cognitive Search to integrate custom OCR models or third-party code into indexing pipelines, allowing for specialized document processing.
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4. Full-Text Search for Scanned Documents:

   1. Use Case: Make scanned images (JPG, PNG, bitmap) fully searchable.
   2. Implementation: Utilize OCR to extract text from scanned images, enabling full-text search capabilities within these documents, enhancing user experience and content discoverability.
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5. Improved PDF Text Extraction:

   1. Use Case: Enhance text extraction from PDFs with combined image and text.
   2. Implementation: Apply OCR to process PDFs that may not parse correctly using standard methods, ensuring accurate extraction of information from both scanned and native PDF formats.
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6. Information Extraction from Unstructured Documents:

   1. Use Case: Extract meaningful content or context hidden in large unstructured or semi-structured documents.
   2. Implementation: Leverage OCR to identify and extract relevant information from complex documents, enabling the creation of structured data for further analysis or integration into business processes.
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7. Enhanced Metadata Creation:

   1. Use Case: Create new information from raw content in unstructured documents.
   2. Implementation: Use computer vision to recognize and extract metadata from images or documents, enabling the generation of additional context and enhancing the overall understanding of the content.
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8. Customized Text Processing Pipelines:

   1. Use Case: Tailor text processing pipelines to specific business needs.
   2. Implementation: Integrate OCR into Azure architecture to create custom text processing pipelines, allowing organizations to adapt document processing workflows based on unique requirements.