A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images

Thoma, Felix; Bayer, Johannes; Li, Yakun; Andreas, Dengel

doi:10.1007/978-3-030-86198-8_2

Cited by 6 publications

(3 citation statements)

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“…This section presents the results of the proposed method and discusses its effectiveness in terms of accuracy and robustness. In addition to our efforts, it's noteworthy to mention the dataset presented in [48]. This dataset, while comprehensive and public, incorporates both electrical and electronic components.…”

Section: Creating and Annotating A Dataset Of Schematicsmentioning

confidence: 99%

“…We developed a comprehensive dataset to tackle the scarcity of annotated circuit schematic data, which we later fine-tuned for more accurate object-detection across varied sizes. In contrast to the public dataset presented in [48], our dataset is exclusively focused on electrical components and covers a wider range of conventional drawing styles. Efforts have been made to ensure its balance and comprehensiveness, which enhances its robustness and generalizability, consequently improving the reliability of trained object-detection models.…”

Section: Complexity and Propensity Towards False Positivesmentioning

confidence: 99%

“…Despite these advancements, the field continues to grapple with challenges such as the detection of small objects and variations in drawing styles [50]. The literature also highlights the scarcity of comprehensive and annotated datasets for electrical schematic interpretation, which has been a significant impediment to the development of robust and generalizable models [48]. This review underscores the need for a novel approach that can effectively address these challenges, particularly in the context of small object detection in electrical schematics.…”

Section: Complexity and Propensity Towards False Positivesmentioning

confidence: 99%

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ElectroNet: An Enhanced Model for Small-Scale Object Detection in Electrical Schematic Diagrams

Uzair,

Chai,

Rassau

2023

Preprint

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This study presents the design and development of a highly accurate and efficient approach for interpreting both hand-drawn and printed electric-circuit schematics. It addresses critical challenges in the field such as the lack of annotated data forobject-detection in schematics. To mitigate this, we have created an extensive dataset, comprising of 23 classes of electric-circuit elements, textual elements with units, prefixes, decimal points, and an array of various handwriting and printing styles with a total of 198495 annotations in the dataset. This rich and diverse dataset ensures the robustness of our system against different styles of circuit drawings. We combined our dataset with the MNIST dataset to enhance the model’s resilience to diverse writing styles. Another obstacle faced in this domain is the accurate recognition and assignment of textual labels in schematics. To overcome this, we employed object-detection methodologies, leveraging use of hybrid model. It has proven effective in analyzing line diagrams and incorporating various language OCRs. Despite initial success with minor parameter adjustments, a shortfall in the accuracy of object-detection was identified when significant size variations occurred between objects. To address this, we trained a new model, leading to significant improvements in the recognition of circuit elements and textual symbols across all sizes, with high precision. This enhanced system showed an overall correct simulation rate of 92%, and an overall correct recognition rate of 94% for textual symbols. This work establishes the efficacy of employing object-detection techniques for interpreting electric schematics while also underlining the challenges and potential avenues for future research in this domain. Our approach has shown notable improvements in recognizing electric-circuit drawings, particularly for smaller components, and opens avenues for further advancements in the field of circuit recognition and analysis.

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Section: Creating and Annotating A Dataset Of Schematicsmentioning

confidence: 99%

Section: Complexity and Propensity Towards False Positivesmentioning

confidence: 99%