ConvNet-Based Optical Recognition for Engineering Drawings

Andrew Brock, Theodore Lim, James Millar Ritchie, Nicholas Weston

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.
Original languageEnglish
Title of host publicationASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
PublisherAmerican Society of Mechanical Engineers
ISBN (Print)9780791858110
Publication statusPublished - 9 Aug 2017
Event37th Computers and Information in Engineering Conference - Cleveland, United States
Duration: 6 Aug 20179 Aug 2017


Conference37th Computers and Information in Engineering Conference
Country/TerritoryUnited States


  • Covnet
  • Optical recognition
  • GD&T
  • Feature recognition


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