37 citations · 115 across the 25 of their papers we have counts for
30 papers
ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains
Artemis Llabrés, Marc Serra Ortega, Tomàs Ockier +6
In this report we present results of the ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains. This competition aimed to advance research in document un…
Learning Quantifiable Visual Explanations Without Ground-Truth
Amritpal Singh, Andrey Barsky, Mohamed Ali Souibgui +2
Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to evaluate due to the lack of g…
Reading in the Dark: Low-light Scene Text Recognition
Xuanshuo Fu, Lei Kang, Ernest Valveny +2
Accurate text recognition in low-light environments is essential for intelligent systems in applications ranging from autonomous vehicles to smart surveillance. However, challenges…
Enhancing Document VQA Models via Retrieval-Augmented Generation
Eric López, Artemis Llabrés, Ernest Valveny
Document Visual Question Answering (Document VQA) must cope with documents that span dozens of pages, yet leading systems still concatenate every page or rely on very large vision-…
LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and Differential Diagnosis
Lei Kang, Xuanshuo Fu, Oriol Ramos Terrades +3
Medical document analysis plays a crucial role in extracting essential clinical insights from unstructured healthcare records, supporting critical tasks such as differential diagno…
DocVXQA: Context-Aware Visual Explanations for Document Question Answering
Mohamed Ali Souibgui, Changkyu Choi, Andrey Barsky +3
We propose DocVXQA, a novel framework for visually self-explainable document question answering. The framework is designed not only to produce accurate answers to questions but als…
xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM
Lei Kang, Xuanshuo Fu, Javier Vazquez-Corral +2
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the critical need for efficient and accurate diagnostic tools. Electrocardiograms (ECGs…
Preserving Privacy Without Compromising Accuracy: Machine Unlearning for Handwritten Text Recognition
Lei Kang, Xuanshuo Fu, Lluis Gomez +3
Handwritten Text Recognition (HTR) is crucial for document digitization, but handwritten data can contain user-identifiable features, like unique writing styles, posing privacy ris…
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
GRIF-DM: Generation of Rich Impression Fonts using Diffusion Models
Lei Kang, Fei Yang, Kai Wang +5
Fonts are integral to creative endeavors, design processes, and artistic productions. The appropriate selection of a font can significantly enhance artwork and endow advertisements…
Image-text matching for large-scale book collections
Artemis Llabrés, Arka Ujjal Dey, Dimosthenis Karatzas +1
We address the problem of detecting and mapping all books in a collection of images to entries in a given book catalogue. Instead of performing independent retrieval for each book…
LayeredDoc: Domain Adaptive Document Restoration with a Layer Separation Approach
Maria Pilligua, Nil Biescas, Javier Vazquez-Corral +3
The rapid evolution of intelligent document processing systems demands robust solutions that adapt to diverse domains without extensive retraining. Traditional methods often falter…
Machine Unlearning for Document Classification
Lei Kang, Mohamed Ali Souibgui, Fei Yang +3
Document understanding models have recently demonstrated remarkable performance by leveraging extensive collections of user documents. However, since documents often contain large…
Multi-Page Document Visual Question Answering using Self-Attention Scoring Mechanism
Lei Kang, Rubèn Tito, Ernest Valveny +1
Documents are 2-dimensional carriers of written communication, and as such their interpretation requires a multi-modal approach where textual and visual information are efficiently…
Privacy-Aware Document Visual Question Answering
Rubèn Tito, Khanh Nguyen, Marlon Tobaben +11
Document Visual Question Answering (DocVQA) has quickly grown into a central task of document understanding. But despite the fact that documents contain sensitive or copyrighted in…
Document Understanding Dataset and Evaluation (DUDE)
Jordy Van Landeghem, Rubén Tito, Łukasz Borchmann +10
We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding…
Hierarchical multimodal transformers for Multi-Page DocVQA
Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny
Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, i…
OCR-IDL: OCR Annotations for Industry Document Library Dataset
Ali Furkan Biten, Rubèn Tito, Lluis Gomez +2
Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of th…
ICDAR 2021 Competition on Document VisualQuestion Answering
Rubèn Tito, Minesh Mathew, C. V. Jawahar +2
In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Docum…
EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA
Arka Ujjal Dey, Ernest Valveny, Gaurav Harit
The open-ended question answering task of Text-VQA often requires reading and reasoning about rarely seen or completely unseen scene-text content of an image. We address this zero-…
Document Collection Visual Question Answering
Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny
Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purch…
InfographicVQA
Minesh Mathew, Viraj Bagal, Rubèn Pérez Tito +3
Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements. In this work, we explore the automatic und…
ICDAR 2019 Competition on Scene Text Visual Question Answering
Ali Furkan Biten, Rubèn Tito, Andres Mafla +6
This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed by any Visual…
Scene Text Visual Question Answering
Ali Furkan Biten, Ruben Tito, Andres Mafla +5
Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims…