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Model flow

The flow of an H2O Document AI model from creation to deployment and consumption can be summarized in the following sequential steps (discussed in the below sections):

H2O Document AI architecture model. The process is broken down in the following steps.

Step 1: Ingest​

Upload your documents to H2O Document AI using the Document AI web interface or API. H2O Document AI lets you handle a wide variety of documents, including:

  • Image scans (faxes in PDF or other formats, pictures with text, and non-editable forms)
  • Documents with embedded text which have text and layout metadata (PDF docs, Word docs, HTML pages)
  • Documents with regular text “left to right/top to bottom” (CSVs, emails, editable forms)

Step 2: Pre-process​

Pre-process documents before training with a set of state-of-the-art computer vision and NLP product features. Pre-processing includes support for:

  • Recognizing and handling embedded text
  • Recognizing and handling logos
  • Page orientation resolution
  • Deskewing
  • Cropping
  • Text formatting optimization
  • Color binarization
  • Addressing input PDF quality challenges

Step 3: Labeling​

Add, improve, and validate document labels:

  • Integrates with common label formats
  • Provides advanced options for validating labels against scored documents and determining labeling sufficiency

Step 4: Train models​

Select the training data set within H2O Document AI, and it will automatically learn the document and create models.

  • Language understanding and layout recognition using learning based on deep learning, transformer architectures, and machine learning
  • AI-ML engine that uses multiple computer vision and NLP algorithms for diverse AI tasks
    • Entity recognition
    • Document and page classification
    • Form understanding
    • Grouping & set identification

Step 5: Post-process​

Post-process to ensure consistency, accuracy, and organization of scored documents. H2O Document AI lets you perform a range of customized post-processing jobs that use AI algorithms vs. rules to ensure high-quality predictions and insights.

  • Organizing prediction sets
  • Confidence and probability measures
  • Datatype standardization – date, times, currency codes, international numerical formats, locations

Step 6: Deploy models​

Publish models into your cloud or on-premises environment of choice. Integrate models into existing systems, processes, and applications via APIs or JSON documents.

Step 7: Consume​

After deploying your models, you can:

  • Consume the model through business apps
  • Store data
  • Batch score in real-time to obtain predictions
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