A two-stage CNN-based hand-drawn electrical and electronic circuit component recognition system

Abstract

Though handwriting recognition is a well-explored research area for decades, there are a few sub-areas of this field that have still not obtained much attention from the researchers. Some examples include recognition of hand-drawn graphics components like circuit components and diagrams. Complete digitization of such handwritten documents is not possible without automatic conversion of the said circuit diagrams. Besides, to date, in most of the cases for commercial circuit design purposes, concerned people manually enter the components into the simulating software like Cadence, Spice to analyze the circuit and judge its performance. In this work, it has been tried to move one step towards automating this process by recognizing the hand-drawn circuit components which are considered as the most important step for this automation. The present endeavour is to design a two-stage convolutional neural network (CNN)-based model that recognizes the hand-drawn circuit components. In the first stage, all the similar-looking (i.e., similar shape and structure) circuit components are clustered into a single group using visual perception and input from confusion matrix of single-stage CNN-based classification, and in the later stage, the circuit components belong to the same group are classified into their actual classes. The proposed model has been evaluated on a self-made database where 20 different classes of hand-drawn circuit components are considered. The experimental outcome shows that the proposed two-stage classification model provides an accuracy 97.33% which is much higher than the single-stage method, which provides an accuracy of 86.00%.