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Mechanism for Structuring the Data from a Generic Identity Document Image using Semantic Analysis

Published: 17 October 2017 Publication History

Abstract

Nowadays, the enormous variety of identity documents that exist makes it difficult to standardize a system capable of extracting all the information of interest presented by them. Therefore, systems that use templates to classify information based on their positions are limited by the number of templates they could recognize. Thus, in this paper, a novel mechanism intended to automatically classify the major information of interest exposed by generic identity documents is presented. The proposal is created to be easily adaptable to any system capable of detecting and extracting text information from an identity document image. To assign meaning to the text extracted from the identity document, the proposal is based on a novel mechanism to structuring the data using semantic analysis. The mechanism consists of two main steps, first, all the text data are classified as sentences or near sentences based on the Euclidean distance between words; second, the sentences are analyzed to find keywords that allow structuring the information based on its semantic to show it as abstractions. The proposal has been designed to be able to store the data as abstractions of its meaning. This allows improving the scalability of the system and a better use of this information by different services, by the end user or to be interpreted by an automated process of decision-making.

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WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web
October 2017
522 pages
ISBN:9781450350969
DOI:10.1145/3126858
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • SBC: Brazilian Computer Society
  • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
  • CGIBR: Comite Gestor da Internet no Brazil
  • CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2017

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Author Tags

  1. cloud computing
  2. computer vision
  3. id
  4. matlab
  5. ocr
  6. semantic analysis

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  • Short-paper

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Webmedia '17
Sponsor:
  • SBC
  • CNPq
  • CGIBR
  • CAPES
Webmedia '17: Brazilian Symposium on Multimedia and the Web
October 17 - 20, 2017
RS, Gramado, Brazil

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WebMedia '17 Paper Acceptance Rate 38 of 138 submissions, 28%;
Overall Acceptance Rate 270 of 873 submissions, 31%

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