Survey on Advanced Text Extraction Techniques Using OCR and Resume-Based Job Recommendation Systems for Sponsorship Platforms

This review examines cutting-edge research and machine learning techniques that support platforms, along with optical character recognition (OCR) technology for effective text extraction. The main objective of the platform is to bridge the gap between students and organizations, offering a shared space for students to receive assistance and for organizations to identify essential roles and skills. Based on this research, the paper illustrates how OCR, iterative parsing, and hybrid consensus models collaborate to enhance the accuracy of matching students with advocates. Contemporary OCR methods transform physical documents into text formats that can be analyzed through machine learning algorithms. Furthermore, this paper investigates a hybrid strategy that merges collaborative and content-based filtering to strengthen the connection between student activities and required sponsors. This approach holds significant potential for developing platforms that not only harness the project's capabilities but also align learners' skills with the specific demands of institutional support.