CV content recognition using YOLOv8 and Tesseract-OCR deep learning

Abstract More effective sorting algorithms are required due to the growing number and variety of resumes in the employment market. Identifying suitable candidates for job openings from a large pool can be both repetitive and time-consuming, potentially leading to missed opportunities or biased selections due to human error. To address this challenge, this study presents a novel CV recognition system that integrates advanced technologies: You Only Look Once for detecting key sections within CVs, Tesseract-OCR for extracting text from these sections, and a series of post-processing steps to correct any text recognition errors. Additionally, the system includes an automated data organization component that stores CV information in a database, facilitating data analytics and search operations. The system was evaluated using a public dataset of 1300 resumes in JPEG, PNG, and JPG formats, sourced from various origins and reflecting diverse formats, languages, and quality levels. Preprocessing was conducted to ensure data consistency and quality. The hyperparameters of the models were optimized using a genetic algorithm. The proposed system significantly enhances efficiency and accuracy in resume sorting, allowing HR teams to concentrate on strategic tasks and streamline the hiring process. Experimental results demonstrate the system’s effectiveness, achieving a mean average precision of 92.1%, a precision rate of 92.2%, and a recall rate of 86.0%.