3,942 research outputs found
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts
Historical palm-leaf manuscript and early paper documents from Indian
subcontinent form an important part of the world's literary and cultural
heritage. Despite their importance, large-scale annotated Indic manuscript
image datasets do not exist. To address this deficiency, we introduce
Indiscapes, the first ever dataset with multi-regional layout annotations for
historical Indic manuscripts. To address the challenge of large diversity in
scripts and presence of dense, irregular layout elements (e.g. text lines,
pictures, multiple documents per image), we adapt a Fully Convolutional Deep
Neural Network architecture for fully automatic, instance-level spatial layout
parsing of manuscript images. We demonstrate the effectiveness of proposed
architecture on images from the Indiscapes dataset. For annotation flexibility
and keeping the non-technical nature of domain experts in mind, we also
contribute a custom, web-based GUI annotation tool and a dashboard-style
analytics portal. Overall, our contributions set the stage for enabling
downstream applications such as OCR and word-spotting in historical Indic
manuscripts at scale.Comment: Oral presentation at International Conference on Document Analysis
and Recognition (ICDAR) - 2019. For dataset, pre-trained networks and
additional details, visit project page at http://ihdia.iiit.ac.in
A Large-Scale Comparison of Historical Text Normalization Systems
There is no consensus on the state-of-the-art approach to historical text
normalization. Many techniques have been proposed, including rule-based
methods, distance metrics, character-based statistical machine translation, and
neural encoder--decoder models, but studies have used different datasets,
different evaluation methods, and have come to different conclusions. This
paper presents the largest study of historical text normalization done so far.
We critically survey the existing literature and report experiments on eight
languages, comparing systems spanning all categories of proposed normalization
techniques, analysing the effect of training data quantity, and using different
evaluation methods. The datasets and scripts are made publicly available.Comment: Accepted at NAACL 201
You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engine
Layout Analysis (the identification of zones and their classification) is the
first step along line segmentation in Optical Character Recognition and similar
tasks. The ability of identifying main body of text from marginal text or
running titles makes the difference between extracting the work full text of a
digitized book and noisy outputs. We show that most segmenters focus on pixel
classification and that polygonization of this output has not been used as a
target for the latest competition on historical document (ICDAR 2017 and
onwards), despite being the focus in the early 2010s. We propose to shift, for
efficiency, the task from a pixel classification-based polygonization to an
object detection using isothetic rectangles. We compare the output of Kraken
and YOLOv5 in terms of segmentation and show that the later severely
outperforms the first on small datasets (1110 samples and below). We release
two datasets for training and evaluation on historical documents as well as a
new package, YALTAi, which injects YOLOv5 in the segmentation pipeline of
Kraken 4.1
Prompt me a Dataset: An investigation of text-image prompting for historical image dataset creation using foundation models
In this paper, we present a pipeline for image extraction from historical
documents using foundation models, and evaluate text-image prompts and their
effectiveness on humanities datasets of varying levels of complexity. The
motivation for this approach stems from the high interest of historians in
visual elements printed alongside historical texts on the one hand, and from
the relative lack of well-annotated datasets within the humanities when
compared to other domains. We propose a sequential approach that relies on
GroundDINO and Meta's Segment-Anything-Model (SAM) to retrieve a significant
portion of visual data from historical documents that can then be used for
downstream development tasks and dataset creation, as well as evaluate the
effect of different linguistic prompts on the resulting detections.Comment: 12 pages, 3 figures, Accepted in ICIAP2023, AI4DH worksho
SIMARA: a database for key-value information extraction from full pages
We propose a new database for information extraction from historical
handwritten documents. The corpus includes 5,393 finding aids from six
different series, dating from the 18th-20th centuries. Finding aids are
handwritten documents that contain metadata describing older archives. They are
stored in the National Archives of France and are used by archivists to
identify and find archival documents. Each document is annotated at page-level,
and contains seven fields to retrieve. The localization of each field is not
available in such a way that this dataset encourages research on
segmentation-free systems for information extraction. We propose a model based
on the Transformer architecture trained for end-to-end information extraction
and provide three sets for training, validation and testing, to ensure fair
comparison with future works. The database is freely accessible at
https://zenodo.org/record/7868059
Osteo-cise: Strong Bones for Life: protocol for a community-based randomised controlled trial of a multi-modal exercise and osteoporosis education program for older adults at risk of falls and fractures
Background : Osteoporosis affects over 220 million people worldwide, and currently there is no \u27cure\u27 for the disease. Thus, there is a need to develop evidence-based, safe and acceptable prevention strategies at the population level that target multiple risk factors for fragility fractures to reduce the health and economic burden of the condition. Methods : The \u27Osteo-cise: Strong Bones for Life\u27 study will investigate the effectiveness and feasibility of a multi-component targeted exercise, osteoporosis education/awareness and behavioural change program for improving bone health and muscle function, and reducing falls risk in community-dwelling older adults at an increased risk of fracture. Men and women aged 60 years or above will participate in an 18-month randomised controlled trial comprising a 12-month structured and supervised community-based program and a 6-month \u27research to practise\u27 translational phase. Participants will be randomly assigned to either the \u27Osteo-cise\u27 intervention or a self-management control group. The intervention will comprise a multi-modal exercise program incorporating high velocity progressive resistance training, moderate impact weight-bearing exercise and high challenging balance exercises performed three times weekly at local community-based fitness centres. A behavioural change program will be used to enhance exercise adoption and adherence to the program. Community-based osteoporosis education seminars will be conducted to improve participant knowledge and understanding of the risk factors and preventative measures for osteoporosis, falls and fractures. The primary outcomes measures, to be collected at baseline, 6, 12, and 18 months, will include DXA-derived hip and spine bone mineral density measurements and functional muscle power (timed stair-climb test). Secondary outcomes measures include: MRI-assessed distal femur and proximal tibia trabecular bone micro-architecture, lower limb and back maximal muscle strength, balance and function (four square step test, functional reach test, timed up-and-go test and 30-second sit-to-stand), falls incidence and health-related quality of life. Cost-effectiveness will also be assessed. Discussion : The findings from the Osteo-cise: Strong Bones for Life study will provide new information on the efficacy of a targeted multi-modal community-based exercise program incorporating high velocity resistance training, together with an osteoporosis education and behavioural change program for improving multiple risk factors for falls and fracture in older adults at risk of fragility fracture.<br /
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