Document Image Classification
22 papers with code • 7 benchmarks • 3 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Document Image Classification
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Libraries
Use these libraries to find Document Image Classification models and implementations
RVL-CDIP
Tobacco-3482
Most implemented papers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
google-research/vision_transformer
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ICLR 2021
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
pytorch/fairseq
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Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
Training data-efficient image transformers & distillation through attention
In this work, we produce a competitive convolution-free transformer by training on Imagenet only.
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
microsoft/unilm
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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.
BEiT: BERT Pre-Training of Image Transformers
We first "tokenize" the original image into visual tokens.
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification
microsoft/unilm
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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.
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
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.
LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding
microsoft/unilm
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In this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding.
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
microsoft/unilm
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In this work, a region-based Deep Convolutional Neural Network framework is proposed for document structure learning.
Revisiting ResNets: Improved Training and Scaling Strategies
tensorflow/tpu
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NeurIPS 2021
Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1. 7x - 2. 7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet.
