Data Rescue for Historical Document Tables Using Semi-Supervised Learning
LG Singh, SE Middleton - 2024 - researchsquare.com
This study uses a novel semi-supervised learning framework to explore Tabular Structure
Recognition (TSR) for digitizing historical documents, specifically employing the …
Recognition (TSR) for digitizing historical documents, specifically employing the …
Enabling Deep Document Image Analysis with Generative Models
K Nikolaidou - 2023 - diva-portal.org
Historical documents are a valuable source of cultural knowledge and can provide
information about previous events, societies, beliefs, and cultures. They can serve as an …
information about previous events, societies, beliefs, and cultures. They can serve as an …
Investigations on Self-supervised Learning for Script-, Font-type, and Location Classification on Historical Documents
In the context of automated classification of historical documents, we investigate three
contemporary self-supervised learning (SSL) techniques (SimSiam, Dino, and VICReg) for …
contemporary self-supervised learning (SSL) techniques (SimSiam, Dino, and VICReg) for …
Visual unsupervised deep learning model design for historical document image analysis
M Omrani Tamrin - 2022 - espace.etsmtl.ca
Historical documents are one of the most crucial influences that drive scientific and historical
development. Some historical documents are present and can be used through classical …
development. Some historical documents are present and can be used through classical …
GloSAT historical measurement table dataset: enhanced table structure recognition annotation for downstream historical data rescue
J Ziomek, SE Middleton - Proceedings of the 6th International Workshop …, 2021 - dl.acm.org
Understanding and extracting tables from documents is a research problem that has been
studied for decades. Table structure recognition is the labelling of components within a …
studied for decades. Table structure recognition is the labelling of components within a …
Historical document processing
This tutorial focuses on recent advances and ongoing developments for historical document
processing. It includes the main challenges involved, the different tasks that have to be …
processing. It includes the main challenges involved, the different tasks that have to be …
Curation of historical Arabic handwritten digit datasets from Ottoman population registers: a deep transfer learning case study
YS Can, ME Kabadayı - … Conference on Big Data (Big Data), 2020 - ieeexplore.ieee.org
With the increasing number of digitization efforts of historical manuscripts and archives,
automatical information retrieval systems need to extract meaning fast and reliably …
automatical information retrieval systems need to extract meaning fast and reliably …
Mambatab: A simple yet effective approach for handling tabular data
Tabular data remains ubiquitous across domains despite growing use of images and texts
for machine learning. While deep learning models like convolutional neural networks and …
for machine learning. While deep learning models like convolutional neural networks and …
An Evaluation of Handwritten Text Recognition Methods for Historical Ciphered Manuscripts
This paper investigates the effectiveness of different deep learning HTR families, including
LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in …
LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in …
Adaptive scaling for archival table structure recognition
Table detection and structure recognition from archival document images remain
challenging due to diverse table structures, complex document layouts, degraded image …
challenging due to diverse table structures, complex document layouts, degraded image …