Grant: Discovery Grants Program - Individual
École de technologie supérieure
$55,000.00
Data-driven modeling for understanding ancient documents from multimodal images
EF 2020-21
Résumé
Nondestructive analysis of documents using multi-spectral (MS) and terahertz (THz) represents a major technical advance in document processing for bringing to life the many ancient documents that are stored in our libraries and museums. For this Discovery Grant, we focus on multimodal historical document image processing and understanding. The overall objective of this research program is to develop a holistic data-driven approach for nondestructive analysis and to efficiently understanding the visual information contained in degraded documents. In addition, we aim to contribute the challenging problem of processing large collections of documents acquired by traditional digitization techniques. The short-term objectives are: OBJ 1) Design a unified image decomposition framework for MS/THz document image analysis; OBJ 2) Devise numerical degradation concealing methods in order to provide clean digitized documents; OBJ 3) Develop a data-driven approach for understanding visual information and discovering latent relations in large collections of ancient documents.
The long-term vision is to explore different non-invasive ranges of the electromagnetic spectrum and contribute a new representation space where gray, color, MS and THz spaces are mapped seamlessly in a unified representation. The outcome of this DG will be the first step towards that goal. The long-term objective will be to propel the THz time domain (THz-TD) modality to embrace multilayer structure processing, which will facilitate the analysis of closed books and wrapped manuscripts without opening them. Deciphering content from one layer to another is challenging from the processing steps to the optimal frequency sampling and dimensionality reduction of the THz images.
We propose a multi-level modeling allowing a fundamental understanding of the interaction between ink and paper and external factors within the same document, as well as the understanding of the content within large collections. This, combined with a unified image analysis framework, will provide a holistic approach to understanding ancient, culturally important manuscripts. It will enable students to experience practical and multidisciplinary problems bridging computational vision and physics. Empowered by new THz-TD image modality and application-agnostic data-driven modeling, the proposed research will take this vision one step further and propel research forward in several disciplines, from the natural sciences to the humanities. Thus, the proposed research will lead to a significant knowledge advance for digital cultural heritage; to significant cost savings; and to innovation in the new era of THz-TD document image modality. Outreach to new user communities, the development and promotion of new research methodologies, stewardship of new technologies and data, and building awareness of the potential that data holds for optimizing research outcomes would ensure Canada's research worldwide competitiveness.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
RGPIN-2019-05230
Name nom
Cheriet, Mohamed
Department département
Génie de la production automatisée
Organization id
53
Institution établissement
École de technologie supérieure
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2019
Award amount
55000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2803
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Knowledge representation
Research subject fr
Répresentation des connaissances
Installment
inst:2-5
Partie
4
Nb partie
5
Application title
Data-driven modeling for understanding ancient documents from multimodal images
Keyword
NSERC
Application summary
Nondestructive analysis of documents using multi-spectral (MS) and terahertz (THz) represents a major technical advance in document processing for bringing to life the many ancient documents that are stored in our libraries and museums. For this Discovery Grant, we focus on multimodal historical document image processing and understanding. The overall objective of this research program is to develop a holistic data-driven approach for nondestructive analysis and to efficiently understanding the visual information contained in degraded documents. In addition, we aim to contribute the challenging problem of processing large collections of documents acquired by traditional digitization techniques. The short-term objectives are: OBJ 1) Design a unified image decomposition framework for MS/THz document image analysis; OBJ 2) Devise numerical degradation concealing methods in order to provide clean digitized documents; OBJ 3) Develop a data-driven approach for understanding visual information and discovering latent relations in large collections of ancient documents.
The long-term vision is to explore different non-invasive ranges of the electromagnetic spectrum and contribute a new representation space where gray, color, MS and THz spaces are mapped seamlessly in a unified representation. The outcome of this DG will be the first step towards that goal. The long-term objective will be to propel the THz time domain (THz-TD) modality to embrace multilayer structure processing, which will facilitate the analysis of closed books and wrapped manuscripts without opening them. Deciphering content from one layer to another is challenging from the processing steps to the optimal frequency sampling and dimensionality reduction of the THz images.
We propose a multi-level modeling allowing a fundamental understanding of the interaction between ink and paper and external factors within the same document, as well as the understanding of the content within large collections. This, combined with a unified image analysis framework, will provide a holistic approach to understanding ancient, culturally important manuscripts. It will enable students to experience practical and multidisciplinary problems bridging computational vision and physics. Empowered by new THz-TD image modality and application-agnostic data-driven modeling, the proposed research will take this vision one step further and propel research forward in several disciplines, from the natural sciences to the humanities. Thus, the proposed research will lead to a significant knowledge advance for digital cultural heritage; to significant cost savings; and to innovation in the new era of THz-TD document image modality. Outreach to new user communities, the development and promotion of new research methodologies, stewardship of new technologies and data, and building awareness of the potential that data holds for optimizing research outcomes would ensure Canada's research worldwide competitiveness.