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Document image classification is the task of classifying documents based on images of their contents.

( Image credit: Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines )

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Greatest papers with code

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

ICLR 2021 huggingface/transformers

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.

 Ranked #1 on Fine-Grained Image Classification on Oxford-IIIT Pets (using extra training data)

DOCUMENT IMAGE CLASSIFICATION FINE-GRAINED IMAGE CLASSIFICATION

LayoutLM: Pre-training of Text and Layout for Document Image Understanding

31 Dec 2019huggingface/transformers

In this paper, we propose the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents.

DOCUMENT IMAGE CLASSIFICATION DOCUMENT LAYOUT ANALYSIS DOCUMENT-LEVEL

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

29 Dec 2020microsoft/unilm

Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.

DOCUMENT IMAGE CLASSIFICATION LANGUAGE MODELLING VISUAL QUESTION ANSWERING

Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification

11 Apr 2017microsoft/unilm

We present an exhaustive investigation of recent Deep Learning architectures, algorithms, and strategies for the task of document image classification to finally reduce the error by more than half.

CLASSIFICATION DOCUMENT IMAGE CLASSIFICATION OBJECT RECOGNITION TRANSFER LEARNING

Improving accuracy and speeding up Document Image Classification through parallel systems

16 Jun 2020javiferran/document-classification

This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions.

CLASSIFICATION DOCUMENT CLASSIFICATION DOCUMENT IMAGE CLASSIFICATION MULTI-MODAL DOCUMENT CLASSIFICATION OPTICAL CHARACTER RECOGNITION TRANSFER LEARNING