Multimodal Document Image Classification

摘要

State-of-the-art methods for document image classification rely on visual features extracted by deep convolutional neural networks (CNNs). These methods do not utilize rich semantic information present in the text of the document, which can be extracted using Optical Character Recognition (OCR). We first study the performance of state-of-the-art text classification approaches when applied to noisy text obtained from OCR. We then show that fusing this textual information with visual CNN methods produces state-of-the-art results on the RVL-CDIP classification dataset.