A Hybrid OCR-XGBoost-Transformer Pipeline for Resume Parsing with Spatial-Semantic Integration - Ed-Daoudi, Rachid; Zakka, Fatima Zahra; Ouqassou, Mouslime; Ettaki, Badia (Informatica, 2025)
This study addresses the automation of resume information extraction using a hybrid Artificial Intelligence (AI) framework that integrates Optical Character Recognition (OCR), Machine Learning, and Deep Learning techniques. The system operates in three stages: text extraction using PaddleOCR, resume section classification via XGBoost, and semantic entity recognition using a Transformers-based Named Entity Recognition (NER) model. The dataset consists of 200 French resumes collected in PDF format and annotated for ten resume section classes and multiple named entities. Evaluation was conducted using standard multi-class classification metrics including accuracy, precision, recall, and F1-score. Experimental results show that XGBoost achieved 96.5% accuracy in section classification, while the Transformers model attained 82% accuracy in semantic entity extraction. This dual-stage pipeline captures both spatial and semantic structures of resumes, offering improved accuracy and adaptability over traditional parsing approaches.