Industrial Optical Character Recognition System in Printing Quality Control of Hot-Rolled Coils Identification

Caldeira, Thaïs; Ciarelli, Patrick Marques; Neto, Gentil Auer

doi:10.1007/s40313-019-00551-1

Cited by 22 publications

(12 citation statements)

References 16 publications

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“…Jain and Sharma [50] several OCR-based nonindustrial applications have been implemented in one system only, but they remain somewhat disconnected one from another. Focusing on the industrial environment, OCR is widely adopted for automatically reading numbers associated to production planning systems [51], especially in those contexts where other identification technologies like radio frequency identification (RFID) might not be reliable or durable enough. In study [52] an attempt of integrating multiple value extraction into one system has been proposed.…”

Section: Multi-purpose Datamentioning

confidence: 99%

One datum and many values for sustainable Industry 4.0: a prognostic and health management use case

Sabbatini

Belli²,

Palma³

et al. 2023

IJECE

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<span>Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future.</span>

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Section: Multi-purpose Datamentioning

confidence: 99%

One datum and many values for sustainable Industry 4.0: a prognostic and health management use case

Sabbatini

Belli²,

Palma³

et al. 2023

IJECE

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“…Weiter werden klassische Verfahren zur Bildanalyse, wie beispielsweise Filter [3] und Kantendetektoren [4] angewandt, um den identifizierten Bildausschnitt weiter zu verarbeiten. Für eine Einführung in diese Verfahren wird auf die angegebenen Quellen verwiesen.…”

Section: Stand Der Technik -Bildanalyse Mit Maschinellen Lernverfahrenunclassified

Maschinelle Lernverfahren zur Digitalisierung manueller Ablese- und Messvorgänge

Mühlbauer¹,

Würschinger²,

Hanenkamp³

et al. 2023

I40M

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Im Fabrikbetrieb kommen häufig Messmittel zum Einsatz, bei denen noch keine automatische Abspeicherung oder Weiterverarbeitung des gemessenen Werts erfolgt. Mitarbeiter müssen in diesem Fall den Messwert manuell er-fassen und weiterverarbeiten. Folgend wird ein Ansatz zur optischen Erfassung und Digitalisierung von Messwerten mithilfe Maschineller Lernverfahren vorgestellt. Dies soll zur Entlastung der Mitarbeiter beitragen, Ablesefehler reduzieren sowie eine automatisierte Dokumentation ermöglichen.

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“…Deep learning is a multi-level representation learning method in which simple but nonlinear modules are combined to change a representation at one level (beginning from the original input) into a higher-level abstract representation 22 . Deep learning-based character recognition is a fundamental and challenging problem in computer vision, and it is widely utilized in business offices 23 , 24 , finance 25 , 26 , medicine 27 , 28 , automotive 29 , 30 , and industrial 31 , 32 . The rich knowledge of context 33 and structured prediction algorithms 34 are combined in these systems to make significant improvements.…”

Section: Related Workmentioning

confidence: 99%

Multiple attention-based encoder–decoder networks for gas meter character recognition

Wang

Ullah

et al. 2022

Sci Rep

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Factories swiftly and precisely grasp the real-time data of the production instrumentation, which is the foundation for the development and progress of industrial intelligence in industrial production. Weather, light, angle, and other unknown circumstances, on the other hand, impair the image quality of meter dials in natural environments, resulting in poor dial image quality. The remote meter reading system has trouble recognizing dial pictures in extreme settings, challenging it to meet industrial production demands. This paper provides multiple attention and encoder–decoder-based gas meter recognition networks (MAEDR) for this problem. First, from the acquired dial photos, the dial images with extreme conditions such as overexposure, artifacts, blurring, incomplete display of characters, and occlusion are chosen to generate the gas meter dataset. Then, a new character recognition network is proposed utilizing multiple attention and an encoder–decoder structure. Convolutional neural networks (CNN) extract visual features from dial images, encode visual features employing multi-head self-attention and position information, and facilitate feature alignment using the connectionist temporal classification (CTC) method. A novel two-step attention decoder is presented to improve the accuracy of recognition results. convolutional block attention module (CBAM) reweights the visual features from the CNN and the semantic features computed by the encoder to improve model performance; long short-term memory attention (LSTM attention) focuses on the relationship between feature sequences. According to experimental data, our system can effectively and efficiently identify industrial gas meter picture digits with 91.1% identification accuracy, faster inference speed, and higher accuracy than standard algorithms. The accuracy and practicality of the recognition can fulfill the needs of instrument data detection and recognition in industrial production, and it has a wide range of applications.

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Industrial Optical Character Recognition System in Printing Quality Control of Hot-Rolled Coils Identification

Cited by 22 publications

References 16 publications

One datum and many values for sustainable Industry 4.0: a prognostic and health management use case

One datum and many values for sustainable Industry 4.0: a prognostic and health management use case

Maschinelle Lernverfahren zur Digitalisierung manueller Ablese- und Messvorgänge

Multiple attention-based encoder–decoder networks for gas meter character recognition

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