Abstract
Digitized documents are increasingly becoming prevalent in various industries. The ability to accurately classify these documents is critical for efficient and effective management. However, digitized documents often come in different formats, making document classification a challenging task. In this paper, we propose a multimodal deep learning approach for digitized document classification. The proposed approach combines both text and image modalities to improve classification accuracy. The model architecture consists of a convolutional neural network (CNN) for image processing and a recurrent neural network (RNN) for text processing. The output features from the two modalities are then merged using a fusion layer to generate the final classification result. The proposed approach is evaluated on a dataset of digitized documents from various industries, including finance, healthcare, and legal fields. The experimental results demonstrate that the multimodal approach outperforms single-modality approaches, achieving high accuracy for document classification. The proposed model has significant potential for applications in various industries that rely heavily on document management systems. For example, in the finance industry, the proposed model can be used to classify loan applications or financial statements. In the healthcare industry, the model can classify patient records, medical images, and other medical documents. In the legal industry, the model can classify legal documents, contracts, and court filings. Overall, the proposed multimodal deep learning approach can significantly improve document classification accuracy, thus enhancing the efficiency and effectiveness of document management systems.