AI-Powered Resume Analysis, PDF Summarization, and Interview Preparation System Using NLP and Deep Learning
DOI:
https://doi.org/10.62643/Keywords:
Natural Language Processing, Text Summarization, Resume Analysis, Transformer Models, Deep Learning, Interview Preparation, Artificial Intelligence, Django Web Application, Text-to-Speech, Document ProcessingAbstract
In today’s competitive job market, candidates require efficient tools to enhance their resumes, prepare for interviews, and quickly understand large volumes of information. This project presents an AI-powered web-based system developed using Django that integrates Natural Language Processing (NLP) and deep learning techniques to assist users in resume evaluation, document summarization, and technical interview preparation.The system provides three primary functionalities. First, it evaluates resumes by extracting text from PDF and Word documents and analyzing the quality using tokenization and spell-checking techniques. The scoring mechanism estimates correctness based on lexical accuracy, helping users improve the quality of their resumes.Second, the system performs automatic document summarization using transformer-based models. By leveraging pre-trained models such as T5 via Hugging Face’s transformer pipeline, the application extracts key information from uploaded PDF documents. This helps users quickly grasp the essential content without reading the entire document. Additionally, the system generates interactive questions from summarized content, enabling active learning.Third, the application includes an intelligent interview preparation module. It conducts a quiz-based technical assessment in programming languages such as Java, C, and C++. Based on user responses, it evaluates performance and provides targeted skill improvement suggestions. The system also enhances user interaction by converting questions into speech using text-to-speech technology, improving accessibility.The backend is implemented using Django, with MySQL for data storage and libraries such as PyPDF2, NLTK, and transformers for text processing. Features like speech synthesis using gTTS and spell correction using autocorrect further enhance usability.This system aims to bridge the gap between traditional resume building and intelligent career preparation tools by integrating AI-driven insights into a unified platform. It offers a scalable solution for students, job seekers, and professionals seeking automated feedback and efficient learning tools.
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