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dc.date.accessioned 2020-02-17T17:34:23Z
dc.date.available 2020-02-17T17:34:23Z
dc.date.issued 2019
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/89180
dc.description.abstract Handwritten Text Recognition is an extensively studied research topic.We implement a widely known binarization method in order to preprocess handwritten text images efficiently and accurately, acquiring adequate binary black-white images for later recognition processes. Afterwards, the characters present in the documents are used to train and evaluate deep-learning mechanisms for the recognition task. Our framework provides good source images for the recognition phase in terms of noise removal and processing of low contrast images. Besides, the process of character recognition is also improved by means of deep-learning techniques. en
dc.format.extent 28-34 es
dc.language en es
dc.subject Handwritten text recognition es
dc.subject Convolutional Neural Networks es
dc.subject Binarization es
dc.subject Document images es
dc.title Towards a Handwritten Text Interpretation Framework for Ancient Spanish Manuscripts en
dc.type Objeto de conferencia es
sedici.identifier.issn 2683-8990 es
sedici.creator.person Xamena, Eduardo es
sedici.creator.person Orozco, Carlos Ismael es
sedici.creator.person Carrasco Cabrera, Gastón es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
sedici.date.exposure 2019-09
sedici.relation.event I Simposio Argentino de Imágenes y Visión (SAIV 2019) - JAIIO 48 (Salta) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)