Abstract
This paper assesses the supporting function of a Machine-based Identification system (MBID) via Optical Character Recognition (OCR) in a Lean manufacturing paradigm. The objective of this paper is to also explore the use of MBID to enable a competitive manufacturing process in a Lean 4.0 environment. Furthermore, a MBID via OCR model is proposed to extract the printed identification number of packages from images captured by a fixed camera in an industrial environment. The method considers different digital image processing techniques to deal with the significant lighting and printing variation observed, followed by a segmentation process that extracts and aligns the characters. Experiments were carried out on a data set consisting of 200 images and achieved an overall detection accuracy of 95% with a very low Character Error Rate (CER) value of 0.0041, clearly supporting the validity and effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shahin, M., Chen, F.F., Bouzary, H., Krishnaiyer, K.: Integration of Lean practices and Industry 4.0 technologies: smart manufacturing for next-generation enterprises. Int. J. Adv. Manuf. Technol. 107(5–6), 2927–2936 (2020). https://doi.org/10.1007/s00170-020-05124-0
Caldeira, T., Ciarelli, P.M., Neto, G.A.: Industrial optical character recognition system in printing quality control of hot-rolled coils identification. J. Control Automation Electr. Syst. 31(1), 108–118 (2019). https://doi.org/10.1007/s40313-019-00551-1
Islam, N., Islam Z, Noor, N.: a survey on optical character recognition system. J. Inf. Commun. Technol. (2017). https://doi.org/10.48550/arXiv.1710.05703
Song, K., Wang, M., Liu, L., Wang, C., Zan, T., Yang, B.: Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal. Int. J. Adv. Manuf. Technol. 109(3–4), 929–942 (2020). https://doi.org/10.1007/s00170-020-05587-1
Mudhsh, M., Almodfer, R.: Arabic handwritten alphanumeric character recognition using very deep neural network. Information 8, 105 (2017). https://doi.org/10.3390/info8030105
Pal, K.K., Sudeep, K.S.: Preprocessing for image classification by convolutional neural networks. In: 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1778–1781 (2016)
Kulkarni, N.: Color thresholding method for image segmentation of natural images. Int. J. Image Graph Signal Process 4 (2012). https://doi.org/10.5815/ijigsp.2012.01.04
Bugayong, V.E., Flores Villaverde, J., Linsangan, N.B.: Google tesseract: optical character recognition (OCR) on HDD/SSD labels using machine vision. In: 2022 14th Int Conf Comput Autom Eng ICCAE Comput Autom Eng ICCAE 2022 14th Int Conf On 56–60 (2022). https://doi.org/10.1109/ICCAE55086.2022.9762440
Garcia, M.B., Claour, J.P.: Mobile bookkeeper: personal financial management application with receipt scanner using optical character recognition. In: 2021 1st Conf Online Teach Mob Educ OT4ME Online Teach Mob Educ OT4ME 2021 1st Conf On 15–20 (2021). https://doi.org/10.1109/OT4ME53559.2021.9638794
Motozuka, A., Kawabe, M., Kano, T.: Acquisition of device information for medical devices using optical character recognition (OCR). In: 2022 IEEE 4th Glob Conf Life Sci Technol LifeTech Life Sci Technol LifeTech 2022 IEEE 4th Glob Conf On, pp. 63–64 (2022). https://doi.org/10.1109/LifeTech53646.2022.9754857
Godbole, S., Joijode, D., Kadam, K., Karoshi, S.: Detection of medicine information with optical character recognition using android. In: 2020 IEEE Bangalore Humanit Technol Conf B-HTC Bangalore Humanit Technol Conf B-HTC 2020, pp. 1–6. IEEE (2020). https://doi.org/10.1109/B-HTC50970.2020.9298016
Bicheno, J., Holweg, M.: The Lean Toolbox, 5th edition. A handbook for lean transformation (2016)
Industrial Quality Control of Packages. https://www.kaggle.com/datasets/christianvorhemus/industrial-quality-control-of-packages. Accessed 17 Jul 2022
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 779–788 (2016)
Colter, Z., Fayazi, M., Youbi, Z.B.-E., et al.: Tablext: A combined neural network and heuristic based table extractor. Array 15 (2022). https://doi.org/10.1016/j.array.2022.100220
Salma, S.M., ur Rahim, R., et al.: Development of ANPR framework for Pakistani vehicle number plates using object detection and OCR. Complexity, 1–14 (2021). https://doi.org/10.1155/2021/5597337
Chazhoor, A., Sarobin, V.R.: Intelligent automation of invoice parsing using computer vision techniques. Multimed. Tools Appl. Int. J., 1–21 (2022). https://doi.org/10.1007/s11042-022-12916-x
Laroca, R., Barroso, V., Diniz, M.A., et al.: Convolutional neural networks for automatic meter reading. J. Electron. Imaging 28, 1–14 (2019). https://doi.org/10.1117/1.JEI.28.1.013023
Chesley, E., Marcantonio, J., Pearson, A.: Towards syriac digital corpora: evaluation of tesseract 4.0 for syriac ocr. Hugoye 22, 109–192 (2019)
de Souza, L.F., Sabóia, C.M.G., Marques, A.G., et al: New approach to the detection and recognition of brazilian mercosur plates using haar cascade and tesseract OCR in real images. J. Inf. Assur. Secur. 16, 144–153 (2021)
Mean Average Precision (mAP) Explained: Everything You Need to Know. https://www.v7labs.com/blog/mean-average-precision, https://www.v7labs.com/blog/mean-average-precision. Accessed 1 Aug 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Shahin, M., Hosseinzadeh, A., Chen, F.F., Davis, M., Rashidifar, R., Shahin, A. (2024). Deploying Optical Character Recognition to Improve Material Handling and Processing. In: Silva, F.J.G., Ferreira, L.P., Sá, J.C., Pereira, M.T., Pinto, C.M.A. (eds) Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems. FAIM 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-38165-2_60
Download citation
DOI: https://doi.org/10.1007/978-3-031-38165-2_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-38164-5
Online ISBN: 978-3-031-38165-2
eBook Packages: EngineeringEngineering (R0)