Analyzing the Application of Optical Character Recognition: A Case Study in International Standard Book Number Detection

Imam Fahrur Rozi; Ahmadi Yuli Ananta; Endah Septa Sintiya; Astrifidha Rahma Amalia; Yuri Ariyanto; Arin Kistia Nugraeni

doi:10.30812/matrik.v24i2.4367

Imam Fahrur Rozi Politeknik Negeri Malang, Malang, Indonesia
Ahmadi Yuli Ananta Politeknik Negeri Malang, Malang, Indonesia
Endah Septa Sintiya Politeknik Negeri Malang, Malang, Indonesia
Astrifidha Rahma Amalia Politeknik Negeri Malang, Malang, Indonesia
Yuri Ariyanto Politeknik Negeri Malang
Arin Kistia Nugraeni Politeknik Negeri Malang, Malang, Indonesia

DOI: https://doi.org/10.30812/matrik.v24i2.4367

Keywords: Book Number Detection, International Standard, Optical Character Recognition

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.

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