Document verification is an important process in industries like finance, governance, and education, where the validity of documents such as Aadhaar cards, PAN cards, and academic marksheets needs to be thoroughly authenticated. Manual, error-prone, and less efficient traditional verification processes are the norm. Deep learning has seen the advent of Convolutional Neural Networks (CNNs), which have shown great potential in the automation and accuracy improvement of document verification systems. This paper introduces an end-to-end CNN-based framework specifically developed for reliable document analysis in the context of tasks like text recognition, forgery detection, and feature extraction in multiple types of documents. The system is highly accurate and reliable and provides a scalable solution for real-world verification applications. We also touch upon the issue faced in processing varied document formats and provide directions for future improvements to make automation and generalization even better for document verification.