Handwritten Logic Circuits Analysis Using the YOLO Network and a New Boundary Tracking Algorithm

Abstract

Handwriting analysis has been addressed by researchers for decades, and many advances were achieved in understanding handwritten texts so far. However, some applications have been rarely discussed. One of these applications that has received less attention is the understanding and analyzing of handwritten circuits. Today, with the widespread use of intelligent tools in engineering and educational processes, the need for new and accurate solutions for processing such handwritings is felt more than ever. This paper presents a new method to analyze handwritten logic circuits. In this method, circuit components are first identified using a deep neural network based on YOLO. Then, the connection among these components is recognized using a new simple boundary tracking method. Then, the binary function related to the handwritten circuit is obtained. Finally, the truth table of the logic circuit is generated. We have also created a set of various handwritten logic circuits called JSU-HWLC. The results of the experiments show the proper performance of the proposed method on the collected dataset. The experiments demonstrated that the YOLO algorithm achieved better results than other deep learning methods such as faster R-CNN, Detectron2, and RetinaNet. For this reason, YOLO has been used to identify logic gates in the proposed system.

Automatic SignWriting Recognition: Combining Machine Learning and Expert Knowledge to Solve a Novel Problem

“…A similar problem to ours is faced by [22], who need to recognize a different kind of language: handwritten logical circuit diagrams. In those diagrams, the 2D position of circuit elements is meaningful, and so, as for us, OCR algorithms are not applicable.…”

Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction

“…There are two recent works concerning the reconstruction of electrical and logical circuits [36,37]. The former discusses new advancements in using neural networks for automatically generating simulation-ready electronic circuits from hand-drawn circuit diagrams; the proposed algorithm first identifies circuit components using YOLOv5 object detection, achieving a high accuracy rate of 98.2%.…”

“…Subsequently, it reconstructs the circuit schematic using a novel Hough transform-based approach for node recognition. On the other hand, [37] addresses the relatively understudied area of analyzing handwritten logic circuits; again, a DNN based on YOLO is used to identify the circuit components within the handwritten diagram, then a simple boundary tracking method is employed to recognize the connections among these identified components. The results indicate that the YOLO algorithm outperformed other Deep Learning (DL) methods such as Faster R-CNN, Detectron2, and RetinaNet in identifying logic gates within the proposed system.…”