Efficient Document Image Classification Using Deep Learning Enhance with CNN Architectures

Document image classification has a significant difficulty for the retrieval of digital documents and systems management in recent years. The main goal of this study is to investigate the classification of scanned document images into distinct groups using deep learning techniques. The numerous quantities of information and unpredictability present in the document images are too much for the traditional methods, which usually rely on human feature extraction and conventional machine learning algorithms. VGG16, GoogleNet, DenseNet, and other advanced networks use convolutional neural networks (CNNs) to increase the accuracy and efficiency of document classification tasks. There are many important uses for classifying document images in various domains. It looks after the document management process by classifying a large amount of documents, including emails, invoices, letters, and forms, for efficient processing, retrieval, and storage. The main task includes the challenges of training and validating datasets for the purpose of classifying document images. Next, it is necessary to have a closer look at CNN design, with a particular emphasis on CNNs' ability to learn hierarchical feature representations. Furthermore, CNN has better efficiency in the feature extraction and transfer learning capabilities, and mostly pre-trained CNN models like VGG16, GoogleNet, and DenseNet have been used very often in document image classification. Different visual data contains generalized feature representations so that it could be advantageous for document classification models, and also pre-trained models that were trained on large-scale datasets such as ImageNet were utilized.