  Digitization of scanned Piping and Instrumentation diagrams(P&ID), widely
used in manufacturing or mechanical industries such as oil and gas over several
decades, has become a critical bottleneck in dynamic inventory management and
creation of smart P&IDs that are compatible with the latest CAD tools.
Historically, P&ID sheets have been manually generated at the design stage,
before being scanned and stored as PDFs. Current digitization initiatives
involve manual processing and are consequently very time consuming, labour
intensive and error-prone.Thanks to advances in image processing, machine and
deep learning techniques there are emerging works on P&ID digitization.
However, existing solutions face several challenges owing to the variation in
the scale, size and noise in the P&IDs, sheer complexity and crowdedness within
drawings, domain knowledge required to interpret the drawings. This motivates
our current solution called Digitize-PID which comprises of an end-to-end
pipeline for detection of core components from P&IDs like pipes, symbols and
textual information, followed by their association with each other and
eventually, the validation and correction of output data based on inherent
domain knowledge. A novel and efficient kernel-based line detection and a
two-step method for detection of complex symbols based on a fine-grained deep
recognition technique is presented in the paper. In addition, we have created
an annotated synthetic dataset, Dataset-P&ID, of 500 P&IDs by incorporating
different types of noise and complex symbols which is made available for public
use (currently there exists no public P&ID dataset). We evaluate our proposed
method on this synthetic dataset and a real-world anonymized private dataset of
12 P&ID sheets. Results show that Digitize-PID outperforms the existing
state-of-the-art for P&ID digitization.
