Sep 9–12, 2019

Document understanding: Extracting structured information from financial images and forms

Joy Rimchala (Intuit), TJ Torres (Intuit), Xiao Xiao (Intuit), Hui Wang (Intuit)
11:55am12:35pm Wednesday, September 11, 2019
Location: 230 A

Who is this presentation for?

  • Data scientists and students




In today’s highly automated world of financial services, consumers, self-employed, and small business owners still face the tedious and time-consuming task of entering data manually from paper documents. Intuit’s document understanding platform orchestrates a variety of services and machine learning capabilities using structured and unstructured documents uploaded by users, regardless of format (smartphone photos, PDFs, forms, etc.), and presents high-confidence results back within the company’s product ecosystem.

Four primary components comprise a system in which all documents pass through dynamically, depending on the document use case: preprocessing of documents, optical character recognition as applied to images, classification of document type, and extraction of key fields. Intuit data scientists Joy Rimchala, Xiao Xiao, TJ Torres, and Hui Wang detail the design and modeling methodologies used to build the document understanding platform—and share lessons learned along the way.

Prerequisite knowledge

  • A working knowledge of data science concepts and terminology

What you'll learn

  • Learn to use and scale machine learning technologies to automate categorization and extraction of documents into your systems from documents of all types
Photo of Joy Rimchala

Joy Rimchala


Joy Rimchala is a data scientist in Intuit’s Machine Learning Futures Group working on ML problems in limited-label data settings. Joy holds a PhD from MIT, where she spent five years doing biological object tracking experiments and modeling them using Markov decision processes.

Photo of TJ Torres

TJ Torres


TJ Torres is a data scientist at Intuit, where he works on the ML futures team tackling research problems in the areas of computer vision (CV) and natural language processing (NLP) in order to better customer experience within Intuit’s core products. Previously, he worked as an applied ML researcher, including building fashion recommendation models using computer vision to help understand visual style at Stitch Fix and building models to help automatically analyze issues with sign up conversion at Netflix. He holds a PhD in physics.

Photo of Xiao Xiao

Xiao Xiao


Xiao Xiao is a data scientist in Intuit’s Consumer Group, using ML to enhance customer experience. Xiao holds a PhD in ecology and a MS in statistics, where she applied statistical analysis to study ecological patterns at broad spatial and temporal scales.

Photo of Hui Wang

Hui Wang


Hui Wang is a staff data scientist at Intuit. Previously, he conducted fundamental natural language processing (NLP) research with grants from the National Institute of Standards and Technology (NIST) and the CIA and provided data modeling for investment banks and hedge funds. Hui has a PhD in chemical engineering from Yale.

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