DLC Re-Builder: Sketch Based Recognition and 2-D Conversion of Digital Logic Circuit

Sharma, Maitreyi; Nipane, Sonal; Rachita,; Jariwala, Krupa N.; Khade, Rasika

doi:10.1007/978-981-16-0404-1_15

Cited by 1 publication

(3 citation statements)

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“…It should be noted that the accuracy of the proposed method has been calculated using the sample images of logic gates provided by ROY et al [2], and the accuracy of the other methods is obtained from [2]. Table 5 shows the class-level and also average values of precision in comparison with an RCNN-based method [10]. To have a fair comparison, this method has been implemented on JSU-HWLC using the hyperparameters provided by Sharma [10].…”

Section: Resultsmentioning

confidence: 99%

“…Table 5 shows the class-level and also average values of precision in comparison with an RCNN-based method [10]. To have a fair comparison, this method has been implemented on JSU-HWLC using the hyperparameters provided by Sharma [10].…”

Section: Resultsmentioning

confidence: 99%

“…In recent years, deep neural networks have made significant progress and have yielded acceptable results in many applications, including object detection and recognition [10]- [16]. A system that accepts sketched logic circuits as input recognizes various components of the circuit using R-CNN and converts the sketch-based circuit into a 2-D formal graphical format is provided in [10]. This system produces a digitized version of hand-drawn sketched images, but it seems that this system has not yet reached maturity.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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