Development of real-time character recognition system for the steel-iron plant

Park, Sang-Gug; Lee, Jong-Hak

doi:10.1109/ichit.2006.253501

Cited by 4 publications

(1 citation statement)

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“…Machine vision applications in iron and steel enterprises are mainly focus on surface defect detection, achieving quality control [2,3] ,preventing unqualified semi-products into the next process. There are also some researchers applying image processing and pattern recognition to the steel surface printing character encoding detection, such as [4,5] proposed based Karhunen-Loeve transformation (KLT), Support vector machine (SVM) and other methods to identify printing characters on the billet surface, [6,7] proposed respectively the methods based on neural network and Optical Flow method for printing character recognition on steel coil, billet surface, [8] proposed the method of character segmentation and recognition based on gray projection area of filtering and template matching . Currently, these methods are aimed at the after-cooling or normal temperature steel products, and the image segmentation process is mainly based on gray figures.…”

Section: Introductionmentioning

confidence: 99%

Image Capturing and Segmentation Method for Characters Marked on Hot Billets

Zhao

Cao

Meng

2014

AMR

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Real-time detecting information marked on billets is important for automatically manufacturing and management in steelworks. But due to the tough production environments in steel enterprises, capturing and identifying characters marked on hot billets have many challenges. This paper presents a real-time image capturing and segmenting method with machine vision for characters marked on hot billets, and characters area is located based on color information of images. Furthermore, considering the marked characters are often slant, we proposed a kind of characters skew correction method to adjust the alignment of characters, and then segment characters into singles for recognition. Finally, with the proposed method, we have conducted some experiments in Baosteel Company. The result shows that our method can achieve 97% segmentation rate if we select proper image acquisition device and preprocessing algorithm. Additionally, it provides a new way for steel enterprise real-time capturing and segmenting marked characters image.

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Section: Introductionmentioning

confidence: 99%

Image Capturing and Segmentation Method for Characters Marked on Hot Billets

Zhao

Cao

Meng

2014

AMR

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Toward intelligent manufacturing: label characters marking and recognition method for steel products with machine vision

Zhao

Cao

2014

Adv. Manuf.

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Correctly coding materials and identifying characters marked on materials are very important for steel manufacturing industry to realize informatization management and intelligent manufacturing. However, the steel products manufacturing is often in a high temperature environment, and there are a lot of material storage and retrieval processes, workers are not easily close to the environment and complete tasks, so it is a great challenge to automatically mark and identify characters on the steel products. This paper presents a framework of label characters marking and management for steel materials, furthermore, a kind of marked characters online detection and tracking method has been provided based on machine vision. In addition, some experiments have been done in BaoSteel to mark characters on hot billets and recognize them in multi situations, and the results show that the proposed method is practical, and has provided a helpful exploration in obtaining accurate fundamental data for the intelligent manufacturing system in steelworks.

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