Text Detection and Recognition from Piping and Instrumentation Diagrams

Piping and Instrumentation Diagrams (P&IDs) are essential engineering documents used to design and operate industrial processes. They consist of graphical symbols and text annotations that describe the equipment, piping, and control systems within a process. Text recognition and detection in P&IDs is an important task as it enables automatic information extraction and analysis, which can help improve the efficiency of industrial processes. In this paper, we present a novel approach of text detection and recognition in P&IDs using deep learning techniques. Our proposed method consists of two stages: text detection using the deep learning-based algorithm EAST and text recognition using EasyOCR. We evaluate our approach on a P&IDs dataset. Experimental results demonstrate the performance of our approach in detecting and recognizing text from P&IDs.