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Found 37 papers, 17 papers with code
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.
Fetch-A-Set: A Large-Scale OCR-Free Benchmark for Historical Document Retrieval
This paper introduces Fetch-A-Set (FAS), a comprehensive benchmark tailored for legislative historical document analysis systems, addressing the challenges of large-scale document retrieval in historical contexts.
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.
SVGCraft: Beyond Single Object Text-to-SVG Synthesis with Comprehensive Canvas Layout
In response, this work introduces SVGCraft, a novel end-to-end framework for the creation of vector graphics depicting entire scenes from textual descriptions.
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.
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
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.
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches
Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods.
The RPM3D project: 3D Kinematics for Remote Patient Monitoring
This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches.
Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis
Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients.
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
A Generic Image Retrieval Method for Date Estimation of Historical Document Collections
Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others.
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.
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach
This paper presents a novel method for date estimation of historical photographs from archival sources.
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting
In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder.
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition
Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models).
Learning Graph Edit Distance by Graph Neural Networks
The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies.
A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages
In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks.
Hierarchical stochastic graphlet embedding for graph-based pattern recognition
Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques.
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch
In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query.
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks.
e-Counterfeit: a mobile-server platform for document counterfeit detection
This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation.
Product Graph-based Higher Order Contextual Similarities for Inexact Subgraph Matching
Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched.
