Image Processing Technology in Book Metadata Extraction System Using Optical Character Recognition (OCR)

Andi Emil Multazam, Akhmad Qashlim, Muhammad Sarjan

Abstract


Extracting book metadata by retyping the identity of the book, such as the author's name, book title, publisher, and several other identities, is a routine that is carried out repeatedly at the Polewali Mandar district library, this activity takes much of time, using several staff and it turns out that this activity has much potential for input errors. Errors in extracting book metadata will result in errors in the book repository system database, resulting in difficulty finding and using books or book data information. This problem can be solved by creating a book metadata extraction system using image processing technology and OCR. This study aims to design a scanner technology to extract book metadata. Accuracy is carried out in 2 stages, the first validation of image extraction results using the ROC method and the second validation by directly matching the result of extracting the book's metadata with the actual book. The results of this study indicate that the system has worked with an accuracy of 98.78% with an average detection time of 1.49 seconds and has succeeded in presenting the extraction results on the website page. Thus the metadata extraction system with the OCR method can be applied to libraries to input book data.

Keywords


Optical Character Recognition; Book Metadata; Image Processing; Extraction System

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References


D. D. Prayoga, “Automasi Ekstraksi Metadata Buku,” 2020. [Online]. Available: https://repository.uir.ac.id/3731/

Y. Huang, H. Xue, B. Liu, and Y. Lu, Unifying Multimodal Transformer for Bi-directional Image and Text Generation, vol. 1, no. 1. Association for Computing Machinery, 2021. doi: 10.1145/3474085.3481540.

Y. Xu, M. Li, L. Cui, S. Huang, F. Wei, and M. Zhou, “LayoutLM: Pre-training of Text and Layout for Document Image Understanding,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 1192–1200, 2020, doi: 10.1145/3394486.3403172.

M. Levy and A. Subburaj, “Emerging Trends in Data Center Management Automation,” 2021 IEEE 11th Annu. Comput. Commun. Work. Conf. CCWC 2021, pp. 480–485, 2021, doi: 10.1109/CCWC51732.2021.9375837.

N. Pawar, Z. Shaikh, P. Shinde, and Y. P. Warke, “Image to Text Conversion Using Tesseract,” Int. Res. J. Eng. Technol., vol. 6, no. 2, pp. 516–519, 2019.

A. Sophia, “A Deep Learning Technique For Optical Word Recognition For Indic Script,” Int. J. Eng. Innov. Adv. Technol., vol. 3, no. 1, pp. 34–38, 2021, [Online]. Available: https://www.ijeiat.com/images/sliders/72d4c21cc02cc98c08f3c2d556cd70af.pdf

R. R. Santoso, “Prototype Sistem Deteksi Plat Nomor Kendaraan Pada Sistem Perparkiran Berbasis Image Processing Menggunakan Metode Optical Character Recognition (Ocr),” Digit. Repos. Univ. Jember Digit. Repos. Univ. Jember, vol. 1, 2020.

S. Hartanto, A. Sugiharto, and S. N. Endah, “Optical Character Recognition Menggunakan Algoritma Template Matching Correlation,” J. Informatics Technol., vol. 1, no. 1, pp. 11–20, 2012, doi: 10.3810/pgm.1998.03.410.

J. Wang, J. Tang, and J. Luo, “Multimodal Attention with Image Text Spatial Relationship for OCR-Based Image Captioning,” MM 2020 - Proc. 28th ACM Int. Conf. Multimed., pp. 4337–4345, 2020, doi: 10.1145/3394171.3413753.

R. Qureshi, M. Uzair, K. Khurshid, and H. Yan, “Hyperspectral document image processing: Applications, challenges and future prospects,” Pattern Recognit., vol. 90, pp. 12–22, 2019, doi: 10.1016/j.patcog.2019.01.026.

R. Bin Yamin, H. Kusuma, and Tasripan, “Image processing-based braille document copying system as additional feature for ITS’s braille printer,” Procedia Comput. Sci., vol. 197, no. 2021, pp. 230–237, 2022, doi: 10.1016/j.procs.2021.12.136.

S. Tomovic, K. Pavlovic, and M. Bajceta, “Aligning document layouts extracted with different OCR engines with clustering approach,” Egypt. Informatics J., vol. 22, no. 3, pp. 329–338, 2021, doi: 10.1016/j.eij.2020.12.004.

J. F. Fauzi, H. Tolle, and R. K. Dewi, “Implementasi Metode RGB To HSV pada Aplikasi Pengenalan Mata Uang Kertas Berbasis Android untuk Tuna Netra,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 6. pp. 2319–2325, 2018.

L. He, Y. Chao, K. Suzuki, and K. Wu, “Fast connected-component labeling,” Pattern Recognit., vol. 42, no. 9, pp. 1977–1987, 2009, doi: 10.1016/j.patcog.2008.10.013.

S. S. Nurhaliza and L. ETP, “Sistem Pengenalan Karakter Dokumen Secara Otomatis Menggunakan Metode Optical Character Recognition,” Petir, vol. 15, no. 1, pp. 166–175, 2022, doi: 10.33322/petir.v15i1.1610.




DOI: http://dx.doi.org/10.38101/sisfotek.v13i1.865

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