2 citations · 4 across the 10 of their papers we have counts for
13 papers
Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines
Eliott Thomas, Mickael Coustaty, Aurelie Joseph +3
Table extraction from business documents relies on a cascaded pipeline where Table Detection (TD) first localizes tables and Table Structure Recognition (TSR) then recovers their i…
ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition
Eliott Thomas, Tri-Cong Pham, Mickael Coustaty +5
Table Structure Recognition (TSR) aims to recover the row and column layout of tables from document images, a key step in document understanding pipelines. Accurate TSR depends on…
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…
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…
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…
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…
KhmerST: A Low-Resource Khmer Scene Text Detection and Recognition Benchmark
Vannkinh Nom, Souhail Bakkali, Muhammad Muzzamil Luqman +2
Developing effective scene text detection and recognition models hinges on extensive training data, which can be both laborious and costly to obtain, especially for low-resourced l…
Lazy-k: Decoding for Constrained Token Classification
Arthur Hemmer, Mickaël Coustaty, Nicola Bartolo +2
We explore the possibility of improving probabilistic models in structured prediction. Specifically, we combine the models with constrained decoding approaches in the context of to…
Estimating Post-OCR Denoising Complexity on Numerical Texts
Arthur Hemmer, Jérôme Brachat, Mickaël Coustaty +1
Post-OCR processing has significantly improved over the past few years. However, these have been primarily beneficial for texts consisting of natural, alphabetical words, as oppose…
CHIC: Corporate Document for Visual question Answering
Ibrahim Souleiman Mahamoud, Mickael Coustaty, Aurelie Joseph +2
The massive use of digital documents due to the substantial trend of paperless initiatives confronted some companies to find ways to process thousands of documents per day automati…
Evaluation of Neural Network Classification Systems on Document Stream
Joris Voerman, Aurelie Joseph, Mickael Coustaty +2
One major drawback of state of the art Neural Networks (NN)-based approaches for document classification purposes is the large number of training samples required to obtain an effi…
Performance Evaluation of Deep Generative Models for Generating Hand-Written Character Images
Tanmoy Mondal, LE Thi Thuy Trang, Mickaël Coustaty +1
There have been many work in the literature on generation of various kinds of images such as Hand-Written characters (MNIST dataset), scene images (CIFAR-10 dataset), various objec…
ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019
Nibal Nayef, Yash Patel, Michal Busta +8
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the go…