TEXT RECOGNITION ALGORITHM FROM PDF RESUMES TO AUTOMATE THE SELECTION OF CANDIDATES FOR IT PROJECTS

Abstract

Nowadays, the development of technology and the growth of the IT industry are accompanied by an unprecedented demand for highly qualified engineers and IT specialists. In this article, the authors consider the problem of automating the selection of candidates for IT projects and propose a text recognition algorithm from PDF resumes that, using the Python language, greatly simplifies and speeds up the candidate selection process. The algorithm uses modern Natural Language Processing (NLP) tools and libraries to work with PDF files to extract key information from candidates' resumes. It recognizes important data, such as education, skills, contact information, etc., and structures it into an easily understandable format. The results of our study indicate the effectiveness of the proposed algorithm and its ability to quickly and accurately analyze a large number of resumes. This opens up wide opportunities for the implementation of automated candidate selection systems in the IT industry, which increases productivity and promotes the efficient use of HR resources. In the article, we also discuss the potential for the development of this algorithm, including the possibility of expanding the supported languages, improving the skill recognition process, and taking into account the specifics of individual IT industries. We emphasize the importance of integrating machine learning to improve the accuracy and analysis of patterns in candidates' resumes. The authors of the article aim to improve and simplify the process of recruiting candidates for IT projects, which will help enterprises to use their intellectual potential more efficiently and ensure the availability of highly qualified IT specialists.