Deep learning and optical character recognition (OCR) have significantly advanced the automation of ID card information extraction, offering organizations a way to enhance efficiency and reduce costs associated with manual data entry and verification. The article explores various deep learning methods, such as convolutional recurrent neural networks (CRNN), spatial transformer networks (STN-OCR), and graph neural networks (GCNs), each with its strengths in handling challenges like multilingual environments, orientation issues, and scene complexity. These technologies can quickly and accurately digitize information from ID cards, passports, and other documents, integrating seamlessly into existing systems. While models like GCNs have achieved state-of-the-art performance, they require significant research and experimentation to optimize. Nanonets offers a user-friendly OCR API that simplifies the process of building and deploying these solutions without extensive technical knowledge, allowing businesses to automate document processing efficiently.