Search Results for author:
Found 28 papers, 13 papers with code
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.
TransferDoc: A Self-Supervised Transferable Document Representation Learning Model Unifying Vision and Language
The field of visual document understanding has witnessed a rapid growth in emerging challenges and powerful multi-modal strategies.
Ranked #19 on
Document Image Classification
on RVL-CDIP
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.
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.
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.
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.
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 #5 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.
