Recognition of Slab Identification Numbers Using a Deep Convolutional Neural Network
Sang Jun Lee, Sang Woo Kim · 2016
In the steel industries, automated identification of product information is important for an efficient manufacturing process. This paper focuses on the recognition problem for slab identification numbers in factory scenes. The recognition problem in an actual industrial setting is significantly more challenging than character recognition in documents or natural scenes. The objective of this paper is to develop an end-to-end recognition algorithm for slab identification numbers, and a Deep Convolutional Neural Network (DCNN) was utilized to construct an integrated recognition algorithm. The proposed algorithm contains composition of training data and a DCNN model, and a decoding process is proposed to transcribe slab identification numbers. The proposed deep learning based algorithm showed a reliable recognition performance for actual industrial scenes.