Document Image Classification
24 papers with code • 8 benchmarks • 4 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
Latest papers
SUT: a new multi-purpose synthetic dataset for Farsi document image analysis
This paper introduces a new large-scale dataset for Farsi document images, named SUT, which aims to tackle the challenges associated with obtaining diverse and substantial ground-truth data for supervised models in document image analysis (DIA) tasks, such as document image classification, text detection and recognition, and information retrieval.
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
Compared to the masked multi-modal modeling methods for document image understanding that rely on both the image and text modalities, StrucTexTv2 models image-only input and potentially deals with more application scenarios free from OCR pre-processing.
Multimodal Side-Tuning for Document Classification
thezingaro/multimodal-side-tuning
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In this paper, we propose to exploit the side-tuning framework for multimodal document classification.
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding.
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
In this paper, we propose \textbf{LayoutLMv3} to pre-train multimodal Transformers for Document AI with unified text and image masking.
DocXClassifier: High Performance Explainable Deep Network for Document Image Classification
saifullah3396/docxclassifier
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TechArXiv 2022
Our approach achieves a new peak performance in image-based classification on two popular document datasets, namely RVL-CDIP and Tobacco3482, with a top-1 classification accuracy of 94. 17% and 95. 57% on the two datasets, respectively.
DiT: Self-supervised Pre-training for Document Image Transformer
We leverage DiT as the backbone network in a variety of vision-based Document AI tasks, including document image classification, document layout analysis, table detection as well as text detection for OCR.
LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding
huggingface/transformers
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ACL 2022
LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models.
OCR-free Document Understanding Transformer
Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs.
DocFormer: End-to-End Transformer for Document Understanding
shabie/docformer
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ICCV 2021
DocFormer uses text, vision and spatial features and combines them using a novel multi-modal self-attention layer.
