Hand-drawn electronic component recognition using deep learning algorithm

Hand-drawn circuit recognition plays an increasingly important role in circuit design work and electrical knowledge teaching. Hand-drawn electronic component recognition is an indispensable part of hand-drawn circuit recognition. Accurate electronic component recognition ensures accurate circuit recognition. In this paper, a hand-drawn electronic component recognition method using a Convolutional Neural Network (CNN) and a softmax classifier is proposed. The CNN composed of a convolutional layer, an activation layer and an average-pooling layer is designed to extract features of a hand-drawn electronic component image. The kernel function for the CNN is obtained by a sparse auto-encoder method. A softmax classifier is trained for classification based on the features extracted by the CNN. The recognition method can identify rotating electronic components because of the added rotated image and achieve 95% recognition accuracy.