Asset Vision: Extracting Metadata from Engineering Drawing Images

Organizations have traditionally struggled with asset management as a result of not having a complete picture of the location and state of their physical assets. This is largely because critical information such as how an asset was built and should operate is locked in highly technical diagrams and unstructured documents. Asset Vision is a new machine learning solution that automates the asset tag extraction process yielding critical asset information in a more time and cost-effective manner than hiring professional experts. The solution applies techniques from Optical Character Recognition, Natural Language Processing, and Machine Learning to identify, classify, and extract asset information from documents.

Faculty Supervisor:

Radu Craiu

Student:

Partner:

Deloitte Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program: