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20182025
most citedDoc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks

32 citations · 111 across the 25 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.CV2024★ 1 cited

Towards Generative Class Prompt Learning for Fine-grained Visual Recognition

Soumitri Chattopadhyay, Sanket Biswas, Emanuele Vivoli +1

Although foundational vision-language models (VLMs) have proven to be very successful for various semantic discrimination tasks, they still struggle to perform faithfully for fine-…

cs.CV2024

FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text Spotting

Alloy Das, Sanket Biswas, Umapada Pal +2

The proliferation of scene text in both structured and unstructured environments presents significant challenges in optical character recognition (OCR), necessitating more efficien…

cs.CV2024

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…

cs.CV2024★ 1 cited

DocSynthv2: A Practical Autoregressive Modeling for Document Generation

Sanket Biswas, Rajiv Jain, Vlad I. Morariu +5

While the generation of document layouts has been extensively explored, comprehensive document generation encompassing both layout and content presents a more complex challenge. Th…

cs.CV2024

DistilDoc: Knowledge Distillation for Visually-Rich Document Applications

Jordy Van Landeghem, Subhajit Maity, Ayan Banerjee +4

This work explores knowledge distillation (KD) for visually-rich document (VRD) applications such as document layout analysis (DLA) and document image classification (DIC). While V…

cs.CV2024

GeoContrastNet: Contrastive Key-Value Edge Learning for Language-Agnostic Document Understanding

Nil Biescas, Carlos Boned, Josep Lladós +1

This paper presents GeoContrastNet, a language-agnostic framework to structured document understanding (DU) by integrating a contrastive learning objective with graph attention net…

cs.CV2024

SketchGPT: Autoregressive Modeling for Sketch Generation and Recognition

Adarsh Tiwari, Sanket Biswas, Josep Lladós

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 sket…

cs.CV2024

GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation

Ayan Banerjee, Sanket Biswas, Josep Lladós +1

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 struc…