Abstract: This paper introduces a novel approach to text extraction and conversion using PyPDF2 technology, aimed at enhancing literacy education. A web application is developed to facilitate the conversion of images to Excel format, with a focus on leveraging PyPDF2 functionalities. The research investigates various methodologies for image-to-text conversion, highlighting the advantages and challenges associated with PyPDF2 compared to traditional OCR techniques. By addressing identified gaps in existing literature, the study presents a comprehensive methodology consisting of capturing, extracting, recognizing, and convert ing phases within the web application. Unlike conventional OCR methods, PyPDF2 offers improved text processing and segmentation algorithms, resulting in enhanced accuracy and efficiency in text extraction. The web application seamlessly converts uploaded images into editable text, making it a valuable resource for both literacy education and teaching staff in diverse educational settings.
Keywords: Recognition; PyPDF2: Text Extraction
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DOI:
10.17148/IJARCCE.2024.13484
