WordVIS: A Color Worth A Thousand Words
This addresses the issue of underutilization of multi-modal approaches in industry due to high resource demands, offering a more efficient solution for document classification.
The paper tackles the problem of multi-modal document classification requiring large datasets and computational power by embedding textual features into visual space, enabling lightweight image-based classifiers to achieve state-of-the-art results, such as a 4.64% improvement with ResNet50 and a record 91.14% accuracy on the Tobacco-3482 dataset.
Document classification is considered a critical element in automated document processing systems. In recent years multi-modal approaches have become increasingly popular for document classification. Despite their improvements, these approaches are underutilized in the industry due to their requirement for a tremendous volume of training data and extensive computational power. In this paper, we attempt to address these issues by embedding textual features directly into the visual space, allowing lightweight image-based classifiers to achieve state-of-the-art results using small-scale datasets in document classification. To evaluate the efficacy of the visual features generated from our approach on limited data, we tested on the standard dataset Tobacco-3482. Our experiments show a tremendous improvement in image-based classifiers, achieving an improvement of 4.64% using ResNet50 with no document pre-training. It also sets a new record for the best accuracy of the Tobacco-3482 dataset with a score of 91.14% using the image-based DocXClassifier with no document pre-training. The simplicity of the approach, its resource requirements, and subsequent results provide a good prospect for its use in industrial use cases.