Ai-Based Online Hand Drawn Engineering Symbol Classification and Recognition

Priya, Alikapati Keerthi; Gaganashree, N.; Hemalatha, K. N.; Chembeti, Janaki Sutha; Kavitha, T.

doi:10.1007/978-981-16-8512-5_22

Cited by 3 publications

(2 citation statements)

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“…Priya A. K. [28] Need to create a benchmark database of manually drawn circuits. Electronic circuits Dey M. [30] Unability to effectively distinguish between highly similar elements, such as ammeter and voltmeter, can be decreased through the inclusion of novel strategies reflecting the local features.…”

Section: Wartegg Handdrawingsmentioning

confidence: 99%

An Overview of Hand-Drawn Diagram Recognition Methods and Applications

Agrawal,

Jagtap,

Kantipudi

2024

IEEE Access

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Hand-drawn diagrams have been a standard visual communication tool in many disciplines, including architectural design, engineering, and education. The inherent diversity and absence of standardized formats of hand-drawn diagrams make it difficult to recognize them. As a result, there is an increasing need for efficient strategies and approaches for correctly identifying and interpreting hand-drawn diagrams. This research study comprehensively reviews hand-drawn diagram recognition (HDDR) techniques, emphasizing their importance and usefulness in numerous sectors. For the past ten years, articles from the Scopus database on HDDR have been extracted and reviewed. The study explores the approaches, steps, and benchmark datasets available to recognize hand-drawn diagrams. An attempt is made to get insights into the most recent state-of-the-art methodologies, their limits, and potential future advancement directions. This paper also suggests probable solutions to overcome the limitations and develop new techniques for efficiently and robustly recognizing hand-drawn diagrams. INDEX TERMSComputer vision, deep learning, hand-drawn diagram recognition, machine learning, sketches.

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Section: Wartegg Handdrawingsmentioning

confidence: 99%

An Overview of Hand-Drawn Diagram Recognition Methods and Applications

Agrawal,

Jagtap,

Kantipudi

2024

IEEE Access

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“…For example, Dey et al [9] designed a two‐stage CNN, which had a group‐level classification and a component‐level classification, to recognize the hand‐drawn circuit components and showed that the proposed method was able to achieve an accuracy of 97.3%. Similarly, Keerthi Priya et al [25] adopted the VGG16 architecture to classify electronic circuit symbols and achieved an accuracy of 99.2%. Inspired by the exceptional performance and the ease of implementation of deep CNNs, this study adopts a deep CNN‐based architecture for the proposed ISA.…”

Section: Introductionmentioning

confidence: 99%

Image‐based structural analysis for education purposes: A proof‐of‐concept study

Loong

Juan

Chang

2023

Comp Applic In Engineering

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In civil engineering education, hand‐drawn sketches of structural systems are commonly used for response analysis. Based on these sketches, students may determine the response of the structures either by hand calculations or via some specialized software through the construction of finite‐element models. The former method is prone to errors, while the latter method could be time‐consuming for inexperienced students. It would be convenient if the information within the hand‐drawn sketches could be automatically converted into computer‐recognizable objects for further structural analysis. To address this issue, a novel method entitled Image‐based Structural Analysis (ISA) is proposed as a proof of concept to determine the response of a linear‐elastic structure directly from the image of a hand‐drawn sketch. A selective search algorithm and a deep convolutional neural network are adopted to detect relevant objects in the images. Based on the bounding boxes and the classes of the objects, a finite element model is constructed for further structural response analysis. This study demonstrates the proposed ISA method via several hand‐drawn beams under loadings. Results show that the proposed method, which consists of a combination of artificial intelligence and semantic rules, can produce, and analyze structural models from hand‐drawn sketches.

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