ResumeVision: NLP Based Screener

Abstract

Resume screening aims to identify the best candidates for a job and provide users with information about their resume score and areas for development. The body of research on current methods have been examined, and it has been found that while manual screening and traditional systems can be error-prone and inefficient, they are not robust in terms of processing, accuracy, and efficiency. Software that uses natural language processing techniques, such as cosine similarity and TF-IDF, to assess how well each resume matches job requirements and machine learning techniques must be used to compare and rank the candidates' resumes in real-time in order to obtain reliable results. The resumes would serve as the input, and the admin would rank the resumes and provide recommendations for the user. Using optical character recognition (OCR), instantaneous results are generated using real-time OCR. The system that the authors are proposing utilized Python scripts, Android concepts, and the OCR model. Among the many advantages of this system are its high accuracy, lightweight design, security, interpretability, and speed of processing.