Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Gang, Sumyung; Fabrice, Ndayishimiye; Chung, Dae-Won; Lee, Joonjae

doi:10.3390/s21092921

Cited by 25 publications

(19 citation statements)

References 22 publications

(45 reference statements)

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“…This study aims to enhance students' deep learning ability through three rounds of action research in teaching and learning. The targets of the action research were first-year college students, and the content of the research was the design and production of personal resume in the public foundational course “Career Development and Employment Guidance for College Students.” The action research divided participants into the experimental class and control class, with 40 students in each class (Pavlicek et al, 2014 ; Gang et al, 2021 ). In this study, students' deep learning ability was mainly measured in three aspects, namely, students' mastery of knowledge, development of abilities, and enhancement of emotional experience.…”

Section: Relative Workmentioning

confidence: 99%

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Liu

Chen²,

Yao³

2022

Front. Psychol.

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With the emergence of big data, cloud computing, and other technologies, artificial intelligence (AI) technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”

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Section: Relative Workmentioning

confidence: 99%

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

Liu

Chen²,

Yao³

2022

Front. Psychol.

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“…[40] presents on a sizable dataset of PCB images at a production plant and labels coresets of OCR characters. No public link is given, but authors note in [41] that data is available upon request. DEEP-PCB (2019) provides a dataset of annotated substrate defects.…”

Section: 32mentioning

confidence: 99%

FPIC: A Novel Semantic Dataset for Optical PCB Assurance

Jessurun¹,

Dizon-Paradis²,

Harrison³

et al. 2022

Preprint

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The continued outsourcing of printed circuit board (PCB) fabrication to overseas venues necessitates increased hardware assurance capabilities. Toward this end, several automated optical inspection (AOI) techniques have been proposed in the past exploring various aspects of PCB images acquired using digital cameras. In this work, we review state-of-the-art AOI techniques and observed the strong, rapid trend toward machine learning (ML) solutions. These require significant amounts of labeled ground truth data, which is lacking in the publicly available PCB data space. We propose the FICS PBC Image Collection (FPIC) dataset to address this bottleneck in available large-volume, diverse, semantic annotations. Additionally, this work covers the potential increase in hardware security capabilities and observed methodological distinctions highlighted during data collection.

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“…Instead of relying entirely on real data, synthetic data can be created to support the development of text spotting methods for PCB component images. There are some limitations of existing methods in generating synthetic PCB component data, for example, generating synthetic images by solely applying data augmentation methods on actual PCB component images is ineffective for character classes that have not collected any sample [18]. A large dataset could be obtained by creating fully-synthetic text images [17]- [19]; however, these synthetic images do not consider the actual backgrounds of PCB components during data generation.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Data Generation for Text Spotting on Printed Circuit Board Component Images

Liau,

Chi Tay,

Mohamed

et al. 2024

IEEE Access

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Machine vision systems with programmed text detection and text recognition features are useful in manufacturing process to automatically locate and read text markings on mounted printed circuit board (PCB) components. To better handle input images with varying image quality, text quality, and text variations, the robustness of deep learning approach for end-to-end text spotting on PCB component images is worth exploring. However, limitations of public PCB component datasets for such research and imbalance of data in actual collected PCB component datasets hinder the training of deep learning text spotting model, and consequently necessitate the generation of synthetic data. In this study, a synthetic PCB component dataset is generated using our synthetic data generator that adds synthetic text with random character sequences on manually edited PCB component images to elevate the realism of the synthetic images. The synthetic dataset covers 66 character classes while providing synthetic text with diverse text variations in font, style, size, and color. We train an existing text spotting model called Text Perceptron using both real and synthetic datasets to detect and recognize arbitrary-shaped text markings on PCB components. Our synthetic PCB component dataset has improved the text spotting performance of Text Perceptron. The trained model achieves promising text detection result and encouraging end-to-end text spotting F-score on real PCB component images. It also meets an acceptable average inference time per image. Still, the text spotting performance of the trained model needs improvement to realize deployment for PCB component inspection.

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Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Cited by 25 publications

References 22 publications

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

FPIC: A Novel Semantic Dataset for Optical PCB Assurance

Synthetic Data Generation for Text Spotting on Printed Circuit Board Component Images

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