Document digitization - Rethinking it with Machine learning


When you think about Document digitisation from a business optimization process perspective, just performing OCR does not truly solve the problem. We at omni:us are building AI systems to support the insurance industry by handling claims. In order to achieve this we are performing various human-esque activities on so many different types of documents like page / document classification, information extraction, semantic understanding to name few. These activities helping in delivering structured information from highly unstructured documents. This structured information is further used in performing activities such as fraud detection, validation and automated claims settlement.

This talk will outline:
* The problems and approaches we faced when building deep learning networks to solve problems in the information extraction process.
* Thought process on why and how we chose certain deep learning strategies
* The requirement for supervised learning
* Limitations of deep learning networks
* Planning and executing research activities in short cycles
* Evolution of team structures to support AI product building
* Engineering practises required in building AI systems.

San Francisco, United States of America

Nischal Harohalli Padmanabha
Software Engineer by profession, filter kapi drinker by choice.

My research interests include deep learning, large scale engineering and social interactions.