Modernizing Data Processing: CNNs and OCR for Automated Document Classification and Data Extraction

The growing use of identification documents such as Aadhar cards in the digital age has underlined the significance of creating simplified methods for automated classification and data extraction. In this paper, we present a comprehensive exploration and practical implementation of cutting-edge technologies aimed at revolutionizing data analysis, information retrieval, and automation, particularly in the context of identifying documents such as Aadhar cards in the digital age. Our approach integrates Convolutional Neural Networks (CNNs) for classification and the Tesseract OCR engine for data extraction, demonstrating their combined potential for driving significant advancements. We systematically tested and optimized various architectural elements, including different layer configurations, dropout rates, batch normalization placements, and regularizers. Through meticulous experimentation, we achieved remarkable results and substantial progress across multiple dimensions. Notably, our final model, strategically leveraging a hybrid optimization strategy involving SGD, RMSprop, and Adam optimizers, along with architectural refinements, attained an impressive accuracy of 97.52%. This accomplishment underscores the effectiveness of our approach in automated classification and data extraction tasks.