The recruitment industry, valued at $200 billion globally, faces challenges in efficiently matching candidates to job openings due to the diversity of resume formats and the manual nature of data extraction. Traditional resume parsing methods, relying on basic rule heuristics and text matching, often struggle with variations in presentation and language. Advanced technologies like deep learning and computer vision offer promising solutions by enabling more intelligent and automated extraction of resume data, improving the accuracy and efficiency of resume parsing. These methods utilize object detection and optical character recognition (OCR) to convert unstructured resume data into structured formats. Tools like Named Entity Recognition (NER) and Convolutional Neural Networks (CNNs) are employed to identify and classify relevant information from resumes, overcoming challenges such as varying templates and language barriers. Nanonets provides an automated solution to streamline resume parsing by using graph convolutional networks (GCNs) and text embeddings, improving data extraction across different languages and formats, and addressing issues like document quality and data drift, thus optimizing the recruitment process for both employers and job seekers.