Unified deep neural networks for end-to-end recognition of multi-oriented billet identification number

Abstract

In this study, a novel framework for the recognition of a billet identification number (BIN) using deep learning is proposed. Because a billet, which is a semi-finished product, could be rolled, the BIN may be rotated at various angles. Most product numbers, including BIN, are a combination of individual characters. Such product numbers are determined based on the class of each character and its order (or the positioning). In addition, the two pieces of information are constant even if the product number is rotated. Inspired by this concept, the proposed framework of deep neural networks has two outputs. One is for the class of an individual character, and the other is the order of an individual character within BIN. Compared with a previous study, the proposed network requires an additional annotation but does not require additional labor for labeling. The multi-task learning for two annotations has a positive role in the representation learning of a network, which is shown in the experiment results. Furthermore, to achieve a good performance of the BIN identification, we analyzed various networks using the proposed framework. The proposed algorithm was then compared with a conventional algorithm to evaluate the performance of the BIN identification.