most citedNeurosymbolic Information Extraction from Transactional Documents

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL20251 cited

Neurosymbolic Information Extraction from Transactional Documents

Arthur Hemmer, Mickaël Coustaty, Nicola Bartolo +1

This paper presents a neurosymbolic framework for information extraction from documents, evaluated on transactional documents. We introduce a schema-based approach that integrates…

cs.CV2025

Evaluating the Impact of Khmer Font Types on Text Recognition

Vannkinh Nom, Souhail Bakkali, Muhammad Muzzamil Luqman +2

Text recognition is significantly influenced by font types, especially for complex scripts like Khmer. The variety of Khmer fonts, each with its unique character structure, present…

cs.AI2025

QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents

Eliott Thomas, Mickael Coustaty, Aurelie Joseph +4

Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipeline…

cs.CV2025

RAPTOR: Refined Approach for Product Table Object Recognition

Eliott Thomas, Mickael Coustaty, Aurelie Joseph +4

Extracting tables from documents is a critical task across various industries, especially on business documents like invoices and reports. Existing systems based on DEtection TRans…

cs.DC2024

LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models

Mouhamed Amine Bouchiha, Quentin Telnoff, Souhail Bakkali +4

Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable perf…