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Found 29 papers, 16 papers with code
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
We use an efficient data curation process to ensure our data is high-quality and license-permissive.
Recurrent Few-Shot model for Document Verification
This task is particularly challenging when dealing with unseen class of ID, or travel, documents.
Towards Generative Class Prompt Learning for Fine-grained Visual Recognition
Specifically, we propose two novel methods: Generative Class Prompt Learning (GCPL) and Contrastive Multi-class Prompt Learning (CoMPLe).
FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text Spotting
The proliferation of scene text in both structured and unstructured environments presents significant challenges in optical character recognition (OCR), necessitating more efficient and robust text spotting solutions.
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications
This work explores knowledge distillation (KD) for visually-rich document (VRD) applications such as document layout analysis (DLA) and document image classification (DIC).
DocSynthv2: A Practical Autoregressive Modeling for Document Generation
While the generation of document layouts has been extensively explored, comprehensive document generation encompassing both layout and content presents a more complex challenge.
LayeredDoc: Domain Adaptive Document Restoration with a Layer Separation Approach
The rapid evolution of intelligent document processing systems demands robust solutions that adapt to diverse domains without extensive retraining.
SketchGPT: Autoregressive Modeling for Sketch Generation and Recognition
We present SketchGPT, a flexible framework that employs a sequence-to-sequence autoregressive model for sketch generation, and completion, and an interpretation case study for sketch recognition.
GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document Understanding
This paper presents GeoContrastNet, a language-agnostic framework to structured document understanding (DU) by integrating a contrastive learning objective with graph attention networks (GATs), emphasizing the significant role of geometric features.
GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation
Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements.
Synthetic dataset of ID and Travel Document
This paper presents a new synthetic dataset of ID and travel documents, called SIDTD.
A Unified Representation Framework for the Evaluation of Optical Music Recognition Systems
Modern-day Optical Music Recognition (OMR) is a fairly fragmented field.
Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance
The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions.
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes
When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system.
GlobalDoc: A Cross-Modal Vision-Language Framework for Real-World Document Image Retrieval and Classification
Visual document understanding (VDU) has rapidly advanced with the development of powerful multi-modal language models.
Ranked #20 on
Document Image Classification
on RVL-CDIP
Beyond Document Page Classification: Design, Datasets, and Challenges
This paper highlights the need to bring document classification benchmarking closer to real-world applications, both in the nature of data tested ($X$: multi-channel, multi-paged, multi-industry; $Y$: class distributions and label set variety) and in classification tasks considered ($f$: multi-page document, page stream, and document bundle classification, ...).
FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework
Scene Text Editing (STE) is a challenging research problem, that primarily aims towards modifying existing texts in an image while preserving the background and the font style of the original text.
SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation
Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image.
SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation
Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc.
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts.
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks
Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis.
Ranked #7 on
Entity Linking
on FUNSD
Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement
In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement.
DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer
has emerged as an interesting problem for the document analysis and understanding community.
DocEnTr: An End-to-End Document Image Enhancement Transformer
Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties.
Ranked #1 on
Binarization
on H-DIBCO 2011
Graph-based Deep Generative Modelling for Document Layout Generation
One of the major prerequisites for any deep learning approach is the availability of large-scale training data.
DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis
The results highlight that our model can successfully generate realistic and diverse document images with multiple objects.
Ehrhart-Equivalence, Equidecomposability, and Unimodular Equivalence of Integral Polytopes
In this paper, we establish a relationship between Ehrhart-equivalence and other forms of equivalence: the $\operatorname{GL}_n(\mathbb{Z})$-equidecomposability and unimodular equivalence of two integral $n$-polytopes in $\mathbb{R}^n$.
Combinatorics
Fault Area Detection in Leaf Diseases using k-means Clustering
With increasing population the crisis of food is getting bigger day by day. In this time of crisis, the leaf disease of crops is the biggest problem in the food industry. In this paper, we have addressed that problem and proposed an efficient method to detect leaf disease. Leaf diseases can be detected from sample images of the leaf with the help of image processing and segmentation. Using k-means clustering and Otsu's method the faulty region in a leaf is detected which helps to determine proper course of action to be taken. Further the ratio of normal and faulty region if calculated would be able to predict if the leaf can be cured at all.
A Statistical Approach to Adult Census Income Level Prediction
The prominent inequality of wealth and income is a huge concern especially in the United States.
