Recognition of slab identification numbers using a fully convolutional network

In the steel industry, slabs are manufactured with different amounts of alloying elements according to production purposes or final products. Because slabs have similar shapes, product identification is required to prevent inadequate production processes. In many steel mills, paint marking systems are widely used to inscribe slab identification numbers (SINs). As smart factory technology receives more attention in recent years, automatic recognition of SINs becomes more important for factory automation. The recognition of SINs is a challenging problem due to complex background of factory scenes and low quality of characters in SINs. To address this difficulties, this paper proposes a deep learning algorithm for recognizing SINs in factory scenes. Most existing recognition algorithms conduct text detection and classification using separate modules, and errors in each step are accumulated. The proposed algorithm employs a fully convolutional network (FCN) with deconvolution layers to integrate the recognition processes and improve the performance in processing time and accuracy. The main contribution of this work is on a novel type of ground-truth data (GTD) for the training of a FCN to recognize SINs in factory scenes. The relation between an input image and the corresponding GTD is directly trained in the manner of image-to-image training, and the FCN generates a prediction map that contains categorical information of individual pixels in an input image. Experiments were thoroughly conducted on industrial data collected from a steelworks to demonstrate the effectiveness of the proposed algorithm.