The primary objectives of this research are to enable accurate job recommendations, job categorisation, and precise extraction of candidate details, such as names, contact information, and email IDs, along with analysis of strengths, weaknesses, and skill sets. Leveraging machine learning models and large language models, this tool processes resumes to classify applicants into appropriate job categories and recommends potential job fields. Name, phone number, and email ID extractions and resume scorers rely on rule-based and supervised learning techniques, ensuring high accuracy in parsing relevant fields. Furthermore, the extraction of strengths, weaknesses, and skill sets employs advanced Large Language Models (LLMs), to provide recruiters with a well-rounded understanding of each candidate's profile. The results demonstrate that the proposed Resume Analyzer significantly enhances the accuracy and efficiency of resume screening and presents a reliable, scalable solution for modern recruitment needs.
- Can upload resumes in PDF and TXT format without any constraints of page numbers
- Job recommendations
- Job categorisation
- Extraction of candidate details, such as names, contact information, and email IDs
- Extraction of portfolio links if any
- Analysis of strengths and weaknesses of candidate
- Extraction of Skill set