11 research outputs found
The use of new technologies to access to handwritten historical information in digital form. Gale贸n Project
Espa帽ol: La investigaci贸n hist贸rica en archivos obliga a realizar un amplio trabajo de revisi贸n de miles de documentos que, en muchos casos, no tienen relaci贸n con el tema de estudio, generando un importante gasto en tiempo y recursos. Para dar respuesta a este problema en relaci贸n al estudio del patrimonio arqueol贸gico subacu谩tico, desde el CAS-IAPH se ha ideado el Proyecto Gale贸n, cuyo objetivo es desarrollar soluciones innovadoras para consultar grandes conjuntos digitalizados de documentos hist贸ricos manuscritos. Actualmente no es posible la transcripci贸n automatizada de un gran volumen de im谩genes de documentos manuscritos, pero el desarrollo tecnol贸gico en el campo del reconocimiento formal de palabras, puede simplificar este proceso. Para ello se ha ideado un modelo te贸rico de B煤squeda de Palabras Claves (BPC) basado en Grafos de Palabras (GP), que, adem谩s de para el patrimonio cultural mar铆timo, podr铆a utilizarse para otros temas de investigaci贸n. Ingl茅s: Historical research in archives forces to realize an extensive work of reviewing thousands of documents that, in many cases, have no connection with the subject matter, generating a significant expenditure of time and resources. To address this problem in relation to the study of underwater archaeological heritage, from the CAS-IAPH has been devised the Galleon Project, which aims to develop innovative solutions to query large sets of historical documents digitized manuscripts. Nowadays It is not possible the automated transcription of a large volume of images from handwritten documents, but the development in the field of formal recognition of words, can simplify this process. For this we have developed a theoretical model to identify Keywords based on Graphs of Words (GP), which, as well as in the maritime cultural heritage, could be used for any research topic
Spotting Keywords in Offline Handwritten Documents Using Hausdorff Edit Distance
Keyword spotting has become a crucial topic in handwritten document recognition, by enabling content-based retrieval of scanned documents using search terms. With a query keyword, one can search and index the digitized handwriting which in turn facilitates understanding of manuscripts. Common automated techniques address the keyword spotting problem through statistical representations.
Structural representations such as graphs apprehend the complex structure of handwriting. However, they are rarely used, particularly for keyword spotting techniques, due to high computational costs. The graph edit distance, a powerful and versatile method for matching any type of labeled graph, has exponential time complexity to calculate the similarities of graphs. Hence, the use of graph edit distance is constrained to small size graphs.
The recently developed Hausdorff edit distance algorithm approximates the graph edit distance with quadratic time complexity by efficiently matching local substructures. This dissertation speculates using Hausdorff edit distance could be a promising alternative to other template-based keyword spotting approaches in term of computational time and accuracy. Accordingly, the core contribution of this thesis is investigation and development of a graph-based keyword spotting technique based on the Hausdorff edit distance algorithm. The high representational power of graphs combined with the efficiency of the Hausdorff edit distance for graph matching achieves remarkable speedup as well as accuracy. In a comprehensive experimental evaluation, we demonstrate the solid performance of the proposed graph-based method when compared with state of the art, both, concerning precision and speed.
The second contribution of this thesis is a keyword spotting technique which incorporates dynamic time warping and Hausdorff edit distance approaches. The structural representation of graph-based approach combined with statistical geometric features representation compliments each other in order to provide a more accurate system. The proposed system has been extensively evaluated with four types of handwriting graphs and geometric features vectors on benchmark datasets. The experiments demonstrate a performance boost in which outperforms individual systems
Proceedings of the 4th International Workshop on Reading Music Systems
The International Workshop on Reading Music Systems (WoRMS) is a workshop
that tries to connect researchers who develop systems for reading music, such
as in the field of Optical Music Recognition, with other researchers and
practitioners that could benefit from such systems, like librarians or
musicologists.
The relevant topics of interest for the workshop include, but are not limited
to: Music reading systems; Optical music recognition; Datasets and performance
evaluation; Image processing on music scores; Writer identification; Authoring,
editing, storing and presentation systems for music scores; Multi-modal
systems; Novel input-methods for music to produce written music; Web-based
Music Information Retrieval services; Applications and projects; Use-cases
related to written music.
These are the proceedings of the 4th International Workshop on Reading Music
Systems, held online on Nov. 18th 2022.Comment: Proceedings edited by Jorge Calvo-Zaragoza, Alexander Pacha and Elona
Shatr
Mapping (Dis-)Information Flow about the MH17 Plane Crash
Digital media enables not only fast sharing of information, but also
disinformation. One prominent case of an event leading to circulation of
disinformation on social media is the MH17 plane crash. Studies analysing the
spread of information about this event on Twitter have focused on small,
manually annotated datasets, or used proxys for data annotation. In this work,
we examine to what extent text classifiers can be used to label data for
subsequent content analysis, in particular we focus on predicting pro-Russian
and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though
we find that a neural classifier improves over a hashtag based baseline,
labeling pro-Russian and pro-Ukrainian content with high precision remains a
challenging problem. We provide an error analysis underlining the difficulty of
the task and identify factors that might help improve classification in future
work. Finally, we show how the classifier can facilitate the annotation task
for human annotators
DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS
During the lifecycle of mega engineering projects such as: energy facilities,
infrastructure projects, or data centers, executives in charge should take into account
the potential opportunities and threats that could affect the execution of such projects.
These opportunities and threats can arise from different domains; including for
example: geopolitical, economic or financial, and can have an impact on different
entities, such as, countries, cities or companies. The goal of this research is to provide
a new approach to identify and predict opportunities and threats using large and diverse
data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to
inform domain specific foresights. In addition to predicting the opportunities and
threats, this research proposes new techniques to help decision-makers for deduction
and reasoning purposes. The proposed models and results provide structured output to
inform the executive decision-making process concerning large engineering projects
(LEPs). This research proposes new techniques that not only provide reliable timeseries
predictions but uncertainty quantification to help make more informed decisions.
The proposed ensemble framework consists of the following components: first,
processed domain knowledge is used to extract a set of entity-domain features; second,
structured learning based on Dynamic Time Warping (DTW), to learn similarity
between sequences and Hierarchical Clustering Analysis (HCA), is used to determine
which features are relevant for a given prediction problem; and finally, an automated
decision based on the input and structured learning from the DTW-HCA is used to
build a training data-set which is fed into a deep LSTM neural network for time-series
predictions. A set of deeper ensemble programs are proposed such as Monte Carlo
Simulations and Time Label Assignment to offer a controlled setting for assessing the
impact of external shocks and a temporal alert system, respectively. The developed
model can be used to inform decision makers about the set of opportunities and threats
that their entities and assets face as a result of being engaged in an LEP accounting for
epistemic uncertainty
Adapting BLSTM Neural Network Based Keyword Spotting Trained on Modern Data to Historical Documents
Being able to search for words or phrases in historic handwritten documents is of paramount importance when preserving cultural heritage. Storing scanned pages of written text can save the information from degradation, but it does not make the textual information readily available. Automatic keyword spotting systems for handwritten historic documents can fill this gap. However, most such systems have trouble with the great variety of writing styles. It is not uncommon for handwriting processing systems to be built for just a single book. In this paper we show that neural network based keyword spotting systems are flexible enough to be used successfully on historic data, even when they are trained on a modern handwriting database. We demonstrate that with little transcribed historic text, added to the training set, the performance can further be enhanced
EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop
Editor of the proceedings of EVALITA 2016