Resume OCR: How to Use YOLOv5 for Automated Resume Parsing

Employers can save time and effort in resume parsing by utilizing advanced computer vision techniques like YOLOv5, which are particularly effective for two-column resumes, a format that challenges traditional OCR methods. Computer vision and deep learning approaches, such as those developed in this study, enhance the segmentation and extraction of information from resumes by training a neural network on a dataset of 3,541 images, resulting in improved accuracy and reliability over existing methods. The process involved data collection, annotation using the Roboflow platform, data augmentation, and model training, ultimately achieving high performance metrics, including a mean average precision of 73.1%. This approach allows for the accurate segmentation of resumes into individual columns, facilitating more efficient OCR processing and extraction of candidate information.