Invoice 2 Vec: Creating AI to Read Documents - Mark Landry - H2O AI World London 2018

Name: Invoice 2 Vec: Creating AI to Read Documents - Mark Landry - H2O AI World London 2018
Uploaded: 2018-11-02
Duration: 23 min
Description: H2O's Mark Landry presents Invoice2Vec, a project to extract dates and totals from business documents, and why off-the-shelf OCR alone falls short.

Predictive AI
H2O Driverless AI

Overview

In this session, H2O.ai's Mark Landry and Brandon present Invoice2Vec, a three-month project built with a client to extract structured information, like invoice dates and totals, from business documents. They explain why off-the-shelf OCR alone falls short, including a real example where OCR misread a Euro amount, then detail their approach combining object detection models like Faster R-CNN and RetinaNet with NLP classification in Driverless AI. The talk covers the team's struggle to generate enough varied, accurately labeled training data and their plan to feed results back into Driverless AI as reusable recipes.

Key takeaways

Why off-the-shelf OCR isn't enough for document extraction, illustrated by a real OCR error that misread a Euro amount as a different currency
How the team used object detection models, Faster R-CNN and RetinaNet, to locate bounding boxes for fields like invoice dates and numbers
Why generating good training data was the hardest part, and how the team used document templates to multiply labeled examples while managing overfitting
How Driverless AI was used to classify extracted text lines against target fields, reaching high accuracy with minimal manual NLP work
The tradeoffs between two-stage detectors like Faster R-CNN and one-stage detectors like RetinaNet, including RetinaNet's loss function for focusing on hard examples

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