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Azure AI Document Intelligence

Score8.8 out of 10

11 Reviews and Ratings

What is Azure AI Document Intelligence?

Azure AI Document Intelligence (formerly Form Recognizer) learns the structure of forms to intelligently extract text and data. It ingests text from forms, applies machine learning technology to identify keys and tables, and then outputs structured data that includes the relationships within the original file. That way, the user can extract information tailored to specific content, without heavy manual intervention or extensive data science expertise.

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Categories & Use Cases

Who Buys & Uses Azure AI Document Intelligence

Azure AI Document Intelligence Reviews

What users are saying about Azure AI Document Intelligence.
View all Azure AI Document Intelligence reviews

Helping Engineering Teams Unlock Value from Existing Documentation

Incentivized
Verified User
Manager in Information Technology (5001-10,000 employees)

Use Cases and Deployment Scope

Azure AI Document Intelligence is leveraged for runbooks and operational documents. We convert runbooks into knowledge-based repositories for copilot. Document Intelligence will extract the needed text to make documents searchable by AI. We have also used this Hackathon use cases enabling Document intelligence in our sandbox environment.

Pros

  • Document extraction.
  • Custom form recognition.
  • Microsoft integration.

Cons

  • struggles with unstructured documents.
  • Scalability and the potential need for quota increases for large docs.
  • Cost Projections can be impacted by architecture decisions.

Return on Investment

  • Turnaround time to build a run book
  • accuracy of desired run book based on the searching of structed documents either in house or vendor based.
  • Building a searchable engineering knowledge base.

Usability

Alternatives Considered

UiPath Document Understanding

Azure AI Document Intelligence Backbone for Multimodal building

Incentivized
Kharan Raju
Data Scientist in Information Technology at Intellectyx Inc (51-200 employees)

Use Cases and Deployment Scope

We use Azure AI Document Intelligence mainly for Multimodal Gen AI building process, we use pdf document to take images embedding and text embedding for text to text and image based output generation model using OCR process. This is our main use case for our customer. Text embedding will be around the image. We easily take text embedding also image embedding also taken but text image embedding only can be taken through Azure AI Document Intelligence Optical Character Recognition and we do that using its api endpoint and key we take from Azure portal. Then we use that in our Python code, integrate, and get text image embedding. Text around image and text in image embedding helps to give accurate answers for the user based queries.

Pros

  • From the Image the OCR technology use to store embedding properly
  • From the images the OCR technology automatically capturing text is brilliant
  • Also for creating Azure AI Document Intelligence endpoint and keys it is very easy to do

Cons

  • Azure AI Document Intelligence should have used to store image based embedding for multiple languages
  • Images storing part is a different area in our case and only for a few images that part can be increased
  • Cost is high which should be reduced so more users will come and more technology through this will come

Return on Investment

  • Significant reduction in manual processing cost by 40-60%
  • Improved data accuracy and reduce work
  • Scalability is good without cost increase

Usability

Alternatives Considered

Azure AI Content Safety, Azure Blob Storage and Azure AI Search

Other Software Used

Azure AI Content Safety, Azure Blob Storage, Azure AI Search