From Spatio-Temporal Data to Manufacturing System Model

Charpentier, Patrick; Véjar, Andrés

doi:10.1007/s40313-014-0133-7

Cited by 14 publications

(1 citation statement)

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“…In the past decade, the steel enterprises started investing in intelligent solutions, and many large-scale steelworks applied information management and control systems (Wang et al 2006); however, these systems depend on the availability of fundamental data acquired online and from their processes, in which coding and identifying the marked information of materials are crucial to achieve information and automation management (Brewer et al 1999). These data can then be organized, validated and (Charpentier and Véjar 2014).…”

Section: Introductionmentioning

confidence: 99%

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

Caldeira

Ciarelli

Neto

2019

J Control Autom Electr Syst

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This work presents a system designed to detect printing errors and misidentifications on steel coils that could lead to tracking problems and even guide to the delivery of the wrong product to the final client. An optical character recognition system is proposed to extract the printed identification of steel coils from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters originally printed in an arch form, ending with a classification routine based on a convolutional neural network. The proposed system presents an approach to treat lighting variations in images, covering low contrast, darker and brighter images. Experiment carried out on a data set with approximately 20,000 images achieved an accuracy higher than 98%, supporting the validity of the proposed method.

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Section: Introductionmentioning

confidence: 99%