Recognition of slab identification numbers using a deep convolutional neural network

Sang Jun Lee; Sang Woo Kim

doi:10.1109/ICMLA.2016.0128

Recognition of slab identification numbers using a deep convolutional neural network

Sang Jun Lee
, Sang Woo Kim

Division of Electronic Engineering

Pohang University of Science and Technology

Research output: Contribution to conference › Conference paper › peer-review

10 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	718-721
Number of pages	4
ISBN (Electronic)	9781509061662
DOIs	https://doi.org/10.1109/ICMLA.2016.0128
State	Published - 2017.01.31
Event	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 - Anaheim, United States Duration: 2016.12.18 → 2016.12.20

Publication series

Name	Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

Conference

Conference	15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
Country/Territory	United States
City	Anaheim
Period	16.12.18 → 16.12.20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1109/ICMLA.2016.0128

Cite this

Lee, S. J., & Kim, S. W. (2017). Recognition of slab identification numbers using a deep convolutional neural network. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 (pp. 718-721). Article 7838232 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2016.0128

Lee, Sang Jun ; Kim, Sang Woo. / Recognition of slab identification numbers using a deep convolutional neural network. Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 718-721 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016).

@inproceedings{faf961dd06284b7a8067d5b4f2b82b7b,

title = "Recognition of slab identification numbers using a deep convolutional neural network",

abstract = "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.",

author = "Lee, \{Sang Jun\} and Kim, \{Sang Woo\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016 ; Conference date: 18-12-2016 Through 20-12-2016",

year = "2017",

month = jan,

day = "31",

doi = "10.1109/ICMLA.2016.0128",

language = "English",

series = "Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016",

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Lee, SJ & Kim, SW 2017, Recognition of slab identification numbers using a deep convolutional neural network. in Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016., 7838232, Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Institute of Electrical and Electronics Engineers Inc., pp. 718-721, 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Anaheim, United States, 16.12.18. https://doi.org/10.1109/ICMLA.2016.0128

Recognition of slab identification numbers using a deep convolutional neural network. / Lee, Sang Jun; Kim, Sang Woo.
Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 718-721 7838232 (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016).

Research output: Contribution to conference › Conference paper › peer-review

TY - GEN

T1 - Recognition of slab identification numbers using a deep convolutional neural network

AU - Lee, Sang Jun

AU - Kim, Sang Woo

PY - 2017/1/31

Y1 - 2017/1/31

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85015357851

U2 - 10.1109/ICMLA.2016.0128

DO - 10.1109/ICMLA.2016.0128

M3 - Conference paper

AN - SCOPUS:85015357851

T3 - Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

SP - 718

EP - 721

BT - Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016

Y2 - 18 December 2016 through 20 December 2016

ER -

Lee SJ, Kim SW. Recognition of slab identification numbers using a deep convolutional neural network. In Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 718-721. 7838232. (Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016). doi: 10.1109/ICMLA.2016.0128

Recognition of slab identification numbers using a deep convolutional neural network

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