Multimodal Deep Networks for Text and Image-Based Document Classification

Classification of document images is a critical step for accelerating archival of old manuscripts, online subscription and administrative procedures. Computer vision and deep learning have been suggested as a first solution to classify documents based on their visual appearance. However, achieving the fine-grained classification that is required in real-world setting cannot be achieved by visual analysis alone. Often, the relevant information is in the actual text content of the document, although this text is not available in digital form. In this work, we introduce a novel pipeline based on off-the-shelf architectures to deal with document classification by taking into account both text and visual information. We design a multimodal neural network that is able to learn both the image and from word embeddings, computed on noisy text extracted by OCR. We show that this approach allows us to improve single-modality classification accuracy by several points on the small Tobacco3482 and large RVL-CDIP datasets, even without clean text information. We release a post-OCR text classification (https://​github.​com/​Quicksign/​ocrized-text-dataset) that complements the Tobacco3482 and RVL-CDIP ones to encourage researchers to look into multi-modal text/image classification.