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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Nugraeni, Arin Kistia
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Computer Science & IT

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Analyzing the Application of Optical Character Recognition: A Case Study in International Standard Book Number Detection Rozi, Imam Fahrur; Ananta, Ahmadi Yuli; Sintiya, Endah Septa; Amalia, Astrifidha Rahma; Ariyanto, Yuri; Nugraeni, Arin Kistia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 2 (2025)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4367

Abstract

In the era of advanced education, assessing lecturer performance is crucial to maintaining educational quality. One aspect of this assessment involves evaluating the textbooks authored by lecturers. This study addresses the problem of efficiently detecting International Standard Book Numbers (ISBNs) within these textbooks using optical character recognition (OCR) as a potential solution. The objective is to determine the effectiveness of OCR, specifically the Tesseract platform, in facilitating ISBN detection to support lecturer performance assessments. The research method involves automated data collection and ISBN detection using Tesseract OCR on various sections of textbooks, including covers, tables of contents, and identity pages, across different file formats (JPG and PDF) and orientations. The study evaluates OCR performance concerning image quality, rotation, and file type. Results of this study indicate that Tesseract performs effectively on high-quality, low-noise JPG images, achieving an F1 score of 0.97 for JPG and 0.99 for PDF files. However, its performance decreases with rotated images and certain PDF conditions, highlighting specific limitations of OCR in ISBN detection. These findings suggest that OCR can be a valuable tool in enhancing lecturer performance assessments through efficient ISBN detection in textbooks.
Analyzing the Application of Optical Character Recognition: A Case Study in International Standard Book Number Detection Rozi, Imam Fahrur; Ananta, Ahmadi Yuli; Sintiya, Endah Septa; Amalia, Astrifidha Rahma; Ariyanto, Yuri; Nugraeni, Arin Kistia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4367

Abstract

In the era of advanced education, assessing lecturer performance is crucial to maintaining educational quality. One aspect of this assessment involves evaluating the textbooks authored by lecturers. This study addresses the problem of efficiently detecting International Standard Book Numbers (ISBNs) within these textbooks using optical character recognition (OCR) as a potential solution. The objective is to determine the effectiveness of OCR, specifically the Tesseract platform, in facilitating ISBN detection to support lecturer performance assessments. The research method involves automated data collection and ISBN detection using Tesseract OCR on various sections of textbooks, including covers, tables of contents, and identity pages, across different file formats (JPG and PDF) and orientations. The study evaluates OCR performance concerning image quality, rotation, and file type. Results of this study indicate that Tesseract performs effectively on high-quality, low-noise JPG images, achieving an F1 score of 0.97 for JPG and 0.99 for PDF files. However, its performance decreases with rotated images and certain PDF conditions, highlighting specific limitations of OCR in ISBN detection. These findings suggest that OCR can be a valuable tool in enhancing lecturer performance assessments through efficient ISBN detection in textbooks.
Co-Authors Amalia, Astrifidha Rahma Ananta, Ahmadi Yuli Imam Fahrur Rozi Sintiya, Endah Septa Yuri Ariyanto