Resspar: AI-Driven Resume Parsing and Recruitment System using NLP and Generative AI

Artificial Intelligence (AI) is a highly emerging domain in the current scenario. It has numerous applications in various fields and almost every domain started integrating with AI for better and efficient working. In today's scenario resumes playa vital role as it decides the candidate's future scope. The candidate's resume must clearly contain the skills and details expected by the recruiters. Resume parser is always a demanding field for both the applicants and the recruiters. The LLM model takes in prompts or instructions and generates text that corresponds to the relevant information extracted from the resume images. This text generation capability is crucial for parsing and understanding the content of resumes in a structured manner. Generative AI is utilized through the GenAl API provided by Google. The GenAl API is used for tasks such as generating text from images of resumes, where it interprets the visual information and produces structured text output containing relevant details like names, emails, phone numbers, and skills. Resspar aims to streamline the hiring process by developing a web-based Resume Parsing System using Natural Language Processing (NLP) like Language Model (LLM), then it also uses GenAl, and Prompt Engineering Techniques with Python and Flask as the backend Framework. It provides a user-friendly platform that automates the extraction of essential information such as personal information (name, email, phone number) and professional skills from uploaded resumes. Recruitment processes often involve sifting through a large number of resumes to identify suitable candidates for specific job roles or domains. Resspar's functionality includes a user interface for uploading resumes, parsing them using advanced algorithms, storing parsed data in a SQLite database, and offering a filtering mechanism to match candidates with specific job requirements. It identifies the candidates whose skills align with the given criteria. This functionality significantly reduces the time spent manually sifting through resumes, enabling recruiters to focus on assessing the most relevant applicants.