Digital Document Verification System

Abstract: Digital documents are widely used in education, banking, healthcare, government, employment, and business. Ensuring the authenticity and integrity of these documents has become increasingly important due to the growing number of forged, altered, and fraudulent documents. Traditional verification methods rely heavily on manual inspection and comparison with original records, which can be time-consuming, costly, and prone to human errors. Recent developments in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Optical Character Recognition (OCR) have enabled automated document verification with improved accuracy and efficiency. Machine learning algorithms such as Random Forest, Support Vector Machine, Decision Trees, Logistic Regression, and K-Nearest Neighbors can classify genuine and fraudulent documents using extracted features. Deep learning models such as Convolutional Neural Networks (CNNs) can analyze document images and detect manipulation and forgery. This review examines recent advancements in digital document verification, including OCR, image processing, digital signatures, blockchain, biometric verification, cloud computing, and multimodal document analysis. The study also discusses commonly used datasets, verification methods, evaluation metrics, challenges, research gaps, and future research directions. The findings indicate that integrating AI-based verification, secure digital signatures, blockchain, and multimodal analysis can support the development of reliable, secure, scalable, and efficient digital document verification systems

Keywords: Digital Document Verification, Document Authentication, Machine Learning, Deep Learning, Optical Character Recognition, Artificial Intelligence, Blockchain, Digital Signature, Forgery Detection.