Revolutionizing Document Analysis Using AI Based Text Extraction Methods

Year : 2024 | Volume : 14 | Issue : 02 | Page : 40 46
    By

    Preeti Singh,

  • Yashraj Singh,

  • Aditya Singh,

  • Adnan Ahmad,

  1. Assistant Professor, Department of Computer Science and Engineering, Babu Banarasi Das Engineering College, Lucknow, Uttar Pradesh, India, India
  2. Student, Department of Computer Science and Engineering, Babu Banarasi Das Engineering College, Lucknow, Uttar Pradesh, India
  3. Student, Department of Computer Science and Engineering, Babu Banarasi Das Engineering College, Lucknow, Uttar Pradesh, India
  4. Student, Department of Computer Science and Engineering, Babu Banarasi Das Engineering College, Lucknow, Uttar Pradesh, India

Abstract

The AI Document Analyzer is an innovative application created to revolutionize how we engage with and comprehend textual data. By integrating a range of advanced technologies, including Next.js, Drizzle ORM, OpenAI, Stripe, TypeScript, and Tailwind, this tool offers a comprehensive solution for document comprehension. Central to its functionality is a chat-based interface powered by the ChatGPT API, allowing users to engage in natural language conversations with their uploaded documents. This interface not only simplifies access to information but also improves the overall user experience. A standout feature of the AI Document Analyzer is its capability to generate summaries and visualizations of complex documents. By leveraging the natural language processing capabilities of OpenAI, the application can extract and present critical insights in a concise and understandable manner. This is especially beneficial for users who need to quickly understand the main points of lengthy documents without having to go through all the content. The integration of Next.js ensures efficient server-side rendering and a smooth user interface, while Drizzle ORM handles the database interactions seamlessly. This combination guarantees that the application is both swift and dependable. Stripe integration facilitates secure and straightforward transactions, enabling users to access premium features or services with ease. TypeScript is employed for its robust type-checking capabilities, which enhance the reliability and maintainability of the codebase. Tailwind CSS is used to create a modern, responsive, and user-friendly design. Overall, the AI Document Analyzer represents a significant advancement in document analysis and information management. By combining powerful technologies and focusing on user-centric design, it offers an innovative solution for extracting and understanding insights from textual data. Whether for business, academic, or personal use, this tool redefines how we interact with documents, making information more accessible and manageable.

Keywords: AI document analyzer, ChatGPT, natural language processing, text extraction, chatbot, artificial intelligence

[This article belongs to Current Trends in Information Technology ]

How to cite this article:
Preeti Singh, Yashraj Singh, Aditya Singh, Adnan Ahmad. Revolutionizing Document Analysis Using AI Based Text Extraction Methods. Current Trends in Information Technology. 2024; 14(02):40-46.
How to cite this URL:
Preeti Singh, Yashraj Singh, Aditya Singh, Adnan Ahmad. Revolutionizing Document Analysis Using AI Based Text Extraction Methods. Current Trends in Information Technology. 2024; 14(02):40-46. Available from: https://journals.stmjournals.com/ctit/article=2024/view=155340


References

  1. Solanki Amrish. Advancements in Artificial Intelligence: A Comprehensive Review and Future Prospects. International Journal of Artificial Intelligence Research and Development (IJAIRD). 2024; 2(1): 53–64.
  2. Khan MS, Ahmad I. Herbal medicine: current trends and future prospects. In New look to phytomedicine. Academic Press. 2019 Jan 1; 3–13.
  3. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S. Language models are few-shot learners. NIPS’20: Proceedings of the 34th International Conference on Neural Information Processing Systems. Article No.: 159, Pages 1877-1901.
  4. Seymour Tom, Frantsvog Dean, Kumar Satheesh. History Of Search Engines. International Journal of Management & Information Systems (IJMIS). 2011; 15(4): 47–58. 10.19030/ijmis.v15i4.5799.
  5. Følstad Asbjørn, Brandtzaeg Petter. Chatbots and the new world of HCI. Interactions. 2017; 24(4): 38–42. 10.1145/3085558.
  6. Maithili K, Raja SN, Kumar RR, Koli S. A Survey (NLP) Natural Language Processing and Transactions on (NNL) Neural Networks and learning Systems. In E3S Web of Conferences. EDP Sciences. 2023; 430: 01148.
  7. Khan Muskan, Ning Chu, Chang Jung. Natural Language Processing Techniques for Enhancing Information Systems Management. 2023.
  8. Dong-Min Park, Seong-Soo Jeong, Yeong-Seok Seo. Systematic Review on Chatbot Techniques and Applications. J Inf Process Syst. 2022; 18(1): 26–47. Available from: https://jips-k.org/pub-reader/707
  9. Kuhail MA, Bahja M, Al-Shamaileh O, Thomas J, Alkazemi A, Negreiros J. Assessing the Impact of Chatbot-Human Personality Congruence on User Behavior: A Chatbot-based Advising System Case. IEEE Access. 2024 May 20; 12: 71761–71782.
  10. Klopfenstein Lorenz, Delpriori Saverio, Malatini Silvia, Bogliolo Alessandro. The Rise of Bots: A Survey of Conversational Interfaces, Patterns, and Paradigms. DIS’17: Proceedings of the 2017 Conference on Designing Interactive Systems. 2017; 555–565. 10.1145/3064663.3064672.

Regular Issue Subscription Review Article
Volume 14
Issue 02
Received 19/05/2024
Accepted 10/06/2024
Published 05/07/2024



My IP

PlumX Metrics