Make with H2O.ai : Getting Started with H2O Document AI

Name: Make with H2O.ai : Getting Started with H2O Document AI
Uploaded: 2022-08-02
Duration: 47 min
Description: An introduction to H2O Document AI, which trains models to extract structured data from faxes, PDFs, and scans, demoed on a UCSF medical referral use case.

Predictive AI
H2O Document AI

Overview

This session introduces H2O Document AI, a tool for training models that extract structured data from documents like faxes, PDFs, and scanned images. Using a live demo built around a UCSF medical referral use case, the presenter shows how the tool treats documents as datasets, runs OCR to capture text and its position on the page, and supports both entity extraction and page classification models. It also covers labeling, merging annotations with OCR results to train a model, and publishing a scoring pipeline as a REST API.

Key takeaways

How H2O Document AI handles documents that arrive in many different formats and layouts, rather than requiring a fixed template
How the tool's OCR step (Tesseract, PDF text extraction, or the H2O-forked doc TR model) chooses the right method per page, including for scanned faxes with no embedded text
How to label documents in the annotation interface, using labels for token-level entity extraction and classes for page classification
How merging an annotation set with OCR results joins labels to tokens to produce the training data a model needs
How to read model accuracy metrics, including macro versus weighted average, to spot which entity classes need more labeled examples

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