| Title | Deep Learning for Text Detection and Recognition in Complex Engineering Diagrams |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Jamieson, Laura, Moreno-Garcia, Carlos Francisco, Elyan, Eyad |
| Conference Name | 2020 International Joint Conference on Neural Networks (IJCNN) |
| Keywords | character recognition, composability, feature extraction, Human Behavior, human factors, machine learning, Metrics, object detection, Optical character recognition software, pubcrawl, Scalability, Shape, text analytics, Text recognition |
| Abstract | Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper copy, meaning the information contained within the diagrams is not readily accessible to inspect and use for further data analytics. In this paper, we make use of the benefits of recent deep learning advances by selecting models for both text detection and text recognition, and apply them to the digitisation of text from within real world complex engineering diagrams. Results show that 90% of text strings were detected including vertical text strings, however certain non text diagram elements were detected as text. Text strings were obtained by the text recognition method for 86% of detected text instances. The findings show that whilst the chosen Deep Learning methods were able to detect and recognise text which occurred in simple scenarios, more complex representations of text including those text strings located in close proximity to other drawing elements were highlighted as a remaining challenge. |
| DOI | 10.1109/IJCNN48605.2020.9207127 |
| Citation Key | jamieson_deep_2020 |
Deep Learning for Text Detection and Recognition in Complex Engineering Diagrams