Machine-based identification system via optical character recognition

Shahin, Mohammad; Chen, F. Frank; Hosseinzadeh, Ali Zare

doi:10.1007/s10696-023-09497-8

Cited by 11 publications

(5 citation statements)

References 81 publications

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“…I4.0 technologies have enabled real-time monitoring and enhancements in processes [84][85][86]. Businesses and government entities aim to gain a competitive advantage by optimizing output while minimizing expenses.…”

Section: Inspection 40mentioning

confidence: 99%

“…Incorporating ML algorithms such as ANN optimizes a range of operations, including logistics, supply chains, production, and marketing [83]. Real-time monitoring and process enhancements facilitated by Industry 4.0 technologies allow businesses and government entities to enhance productivity and quality, which is crucial for maintaining a competitive edge and minimizing expenses [84][85][86]. Integrating these technologies is pivotal in achieving operational excellence and continually optimizing output while minimizing expenses, as they pave the way for more innovative, more efficient operational processes [87][88][89][90].…”

Section: Inspection 40mentioning

confidence: 99%

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Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

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Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

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Section: Inspection 40mentioning

confidence: 99%

Section: Inspection 40mentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

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“…This includes decision support for production scheduling (Bouzekri et al, 2022), optimization of resource utilization (Tripathi et al, 2022b) and multi-skilled worker assignment (Xin et al, 2015). (Abusaq et al, 2023;Arana-Landín et al, 2023;Marinelli, 2022;Shahin et al, 2023aShahin et al, , 2023bShahin et al, , 2023cShahin et al, , 2023dShahin et al, 2023a;Thiede et al, 2017;Trabucco and De Giovanni, 2021;Tripathi et al, 2022a;Tseng et al, 2021) Source: Author's own work adapt to changing environments and respond quickly to market changes. Importantly, and in line with lean thinking and practice, most of the research here points towards the decentralization of production planning and control systems, increasing flexibility and resilience of operations (Küfner et al, 2021;Rossit et al, 2019;Ulhe et al, 2023).…”

Section: Smart Production Planning and Controlmentioning

confidence: 99%

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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“…This innovation marks a significant advancement in the field of text recognition, promising enhanced accuracy and efficiency for document digitization applications. Shahin Et al [11] explores the integration of Lean manufacturing principles with Industry 4.0 advancements to achieve a competitive edge. It focuses on assessing the role of a Machine-based Identification system (MBID) via Optical Character Recognition (OCR) in Lean manufacturing.…”

Section: Advancementsmentioning

confidence: 99%

Optical Character Recognition (OCR): A Comprehensive Review

2023

IRJMETS

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Optical Character Recognition (OCR) technology has witnessed significant advancements in recent years, driven by the growing demand for digitizing and processing large volumes of printed and handwritten documents. This paper presents a comprehensive overview of the latest developments in OCR, with a focus on key innovations and breakthroughs. Cutting-edge techniques, such as deep learning-based approaches, transformer architectures, and attention mechanisms, are discussed, highlighting their role in improving accuracy and efficiency in text recognition tasks. The integration of OCR with other emerging technologies, such as natural language processing and computer vision, is also explored, opening new avenues for enhancing OCR capabilities. Additionally, the paper delves into various applications of OCR across domains, including document processing, data extraction, language translation, and accessibility for visually impaired individuals. By understanding these recent advancements, researchers and practitioners can harness OCR's full potential to address diverse challenges in document digitalization and information retrieval processes.

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Machine-based identification system via optical character recognition

Cited by 11 publications

References 81 publications

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Optical Character Recognition (OCR): A Comprehensive Review

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