Licheng Liu’s research while affiliated with Hunan University and other places

The automatic recognition of labels marked on steel slab surfaces is of significance to information management and intelligent manufacturing in steel plants. However, it is not an easy task due to complex factors like low printing quality, motion distortion and thermal blurring, especially while handling the prone-to-deform dot-matrix labels generated by a hot spray marking (HSM) technique. In this paper, a machine vision system is presented for the HSM dot-matrix label recognition. With a brief description of the imaging system, our emphasis is put on image analysis. First, a coarse-to-fine strategy is applied to locate HSM characters from captured images, where a weighted gravity-center estimation method is extended to search the enclosure of label regions, and an edge projection scheme is adopted to refine the label extraction. Subsequently, a Multidirectional Line Scanning (MLS) method is proposed to determine the boundaries between adjacent dot-matrix characters with tilt, adhesion or dot-missing abnormalities. Finally, by converting the dot-matrix character into a 2D point set, we introduce a Point Cloud registration for DOt-matrix Character (PC4DOC) method to recognize prone-to-deform characters, which appears to accommodate various distortions and abnormalities owing to the inherent deformation correction of affine transformation and fault tolerance of robust correspondence matching. According to our experiments, the proposed method can achieve real-time recognition with an accuracy of 93.84% in spite of severely degraded images and incomplete characters. The system has been installed and run in a steel mill for more than one year, and its stability was also verified.