Image Capturing and Segmentation Method for Characters Marked on Hot Billets

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.

[1]  Yi Lu Murphey,et al.  An intelligent real-time vision system for surface defect detection , 2004, ICPR 2004.

[2]  Mamoru Minami,et al.  Finding and quantitative evaluation of minute flaws on metal surface using hairline , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[3]  Takashi Tanaka,et al.  A neural network recognition system for machine printed characters on coils , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.

[4]  Zhou Ze-kui Design of On-line Automatic Vision Inspection System for Steel Billet Characters , 2006 .

[5]  Changhyun Park,et al.  Development of recognition system for billet identification , 2007, SICE Annual Conference 2007.

[6]  Sang-gug Park,et al.  Development of real-time character recognition system for the steel-iron plant , 2006, 2006 International Conference on Hybrid Information Technology.

[7]  Qijie Zhao,et al.  Toward intelligent manufacturing: label characters marking and recognition method for steel products with machine vision , 2014, Advances in Manufacturing.

[8]  SungHoo Choi,et al.  Text localization using valid optical flow for recognition of slab numbers , 2010, ICCAS 2010.