Multimodal deep networks for text and image-based document classification

07/15/2019 ∙ by Nicolas Audebert, et al. ∙ QUICKSIGN 16

Classification of document images is a critical step for 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. We design a multimodal neural network that is able to learn from word embeddings, computed on text extracted by OCR, and from the image. We show that this approach boosts pure image accuracy by 3 Tobacco3482 and RVL-CDIP augmented by our new QS-OCR text dataset (https://github.com/Quicksign/ocrized-text-dataset), even without clean text information.

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document-image-classification

Document image classification on the Tobacco-3482 dataset using multi-modal CNNs


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1 Introduction

Figure 1: Multimodal classifier for hybrid text/image classification. Training is performed end-to-end on both textual and visual features.

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(e) Document samples from the RVL-CDIP [1] dataset with corresponding text extracted by Tesseract OCR.
(a) Questionnaire