International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
Artificial Intelligence Powered PDF Summarization with Audio and Multilingual Text Translation Dr. Pankaj Kumar1, Purnesh S Gowda2, Dushyanth L3 ,Pavan B4,Tilak R5 1 Assistant Professor, ISE, Acharya Institute Of Technology, Karnataka, India 2 B.E Student, ISE, Acharya Institute Of Technology, Karnataka, India 3 B.E Student, ISE, Acharya Institute Of Technology, Karnataka, India 4 B.E Student, ISE, Acharya Institute Of Technology, Karnataka, India 5 B.E Student, ISE, Acharya Institute Of Technology, Karnataka, India
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Abstract - In this project, we present an automated
Processing (NLP) is a computer science and linguistics field that deals with the interactions between computers and natural languages. It is originated as a branch simple extraction methods for text-based PDFs. While OCR technologies have advanced significantly, they are often computationally expensive and prone to errors, especially when dealing with complex layouts or low quality images. On the other hand, traditional text extraction tools, such as PyPDF2, are limited to text-based PDFs and may struggle to handle cases where the text is embedded in nonstandard formats or non-selectable fonts. Additionally, once the text is extracted, translating the content into multiple languages introduces further complexity, especially when considering the diverse linguistic nuances and context-specific translation requirements.
pipeline for extracting text from PDF documents, translating it into a target language, and exporting the translated output in a structured JSON format. The system leverages the capabilities of the PyPDF2 library for extracting textual content from PDF files, and Google’s Translator API for performing accurate and efficient translations into diverse languages, such as Kannada. The translated text is then saved in JSON format, ensuring easy integration with other applications or workflows. This approach streamlines the process of handling multilingual textual data from PDFs, making it particularly valuable for researchers, educators, and organizations working with diverse linguistic datasets. The system’s modular design allows for adaptability across domains, enabling seamless customization for additional features such as summarization or audio conversion. The proposed workflow significantly reduces the manual effort involved in translating and managing multilingual content while maintaining high accuracy and scalability.
Manual methods of handling such data require separate steps for extraction, translation, and storage are both time consuming and prone to human error. For organizations or researchers working with large datasets that require multilingual processing, these manual workflows can become a significant bottleneck, limiting the ability to scale and quickly respond to data needs. Furthermore, the lack of integration between various tools, such as PDF extraction and translation services, increases the complexity and inefficiency of the overall process.
Key Words: Natural Language Processing NLP, PDF
Text Extraction, Multilingual Automated Content Processing
Translation,
1. INTRODUCTION
This project presents an integrated, automated pipeline that addresses these challenges by combining text extraction, multilingual translation, and structured data storage into a seamless workflow. The proposed system leverages the PyPDF2 library for efficient extraction of textual data from PDF files and employs Google’s Translator API to translate the extracted content into a target language, such as Kannada. By using a structured JSON format for storing the translated data, the system ensures easy integration with other applications or services for further processing, such as summarization or text-to speech conversion.
The rapid growth of digital content has transformed how information is distributed, consumed, and processed across various sectors. Among the most widely used formats for sharing and storing textual information, Portable Document Format (PDF) documents remain a prevalent choice due to their cross-platform compatibility and ability to preserve formatting. However, despite their widespread adoption, extracting useful information from PDFs can be an arduous task, particularly when the content is large, complex, or requires multilingual handling. The inherent challenges in dealing with PDF documents arise from the non-linear structure of the text, embedded graphics, and formatting elements that make automated extraction processes difficult. Text extraction from PDFs typically relies on Optical Character Recognition (OCR) for scanned documents or because it is free from linguistic ambiguity. Natural Language
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The modular design of the system allows for flexibility and scalability, enabling users to extend its functionality according to specific needs.
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