OcrV1, Main, Exploration, bibRecord, 001E32

Fast and accurate handwritten character recognition using approximate nearest neighbours search on large databases

Identifieur interne : 001E32 ( Main/Exploration ); précédent : 001E31; suivant : 001E33

Fast and accurate handwritten character recognition using approximate nearest neighbours search on large databases

Auteurs : J. C. Perez-Cortes [Espagne] ; R. Llobet [Espagne] ; J. Arlandis [Espagne]

Source :

Lecture notes in computer science [ 0302-9743 ] ; 2000.

RBID : Pascal:00-0499411

Descripteurs français

Pascal (Inist)
- Reconnaissance caractère, Base donnée, Ecriture, Plus proche voisin, Algorithme recherche, Taux erreur, Méthode non paramétrique.
Wicri :
- topic : Base de données.

English descriptors

KwdEn :
- Character recognition, Database, Error rate, Hand writing, Nearest neighbour, Non parametric method, Search algorithm.

Abstract

In this work, fast approximate nearest neighbours search algorithms are shown to provide high accuracies, similar to those of exact nearest neighbour search, at a fraction of the computational cost in an OCR task. Recent studies [26,15] have shown the power of k-nearest neighbour classifiers (k-nn) using large databases, for character recognition. In those works, the error rate is found to decrease consistently as the size of the database increases. Unfortunately, a large database implies large search times if an exhaustive search algorithm is used. This is often cited as a major problem that limits the practical value of the k-nearest neighbours classification method. The error rates and search times presented in this paper prove, however, that k-nn can be a practical technique for a character recognition task.

Affiliations:

Links toward previous steps (curation, corpus...)

to stream PascalFrancis, to step Corpus: 000762
to stream PascalFrancis, to step Curation: 000032
to stream PascalFrancis, to step Checkpoint: 000726
to stream Main, to step Merge: 001F38
to stream Main, to step Curation: 001E32

Le document en format XML

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<term>Taux erreur</term>
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<front><div type="abstract" xml:lang="en">In this work, fast approximate nearest neighbours search algorithms are shown to provide high accuracies, similar to those of exact nearest neighbour search, at a fraction of the computational cost in an OCR task. Recent studies [26,15] have shown the power of k-nearest neighbour classifiers (k-nn) using large databases, for character recognition. In those works, the error rate is found to decrease consistently as the size of the database increases. Unfortunately, a large database implies large search times if an exhaustive search algorithm is used. This is often cited as a major problem that limits the practical value of the k-nearest neighbours classification method. The error rates and search times presented in this paper prove, however, that k-nn can be a practical technique for a character recognition task.</div>
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Fast and accurate handwritten character recognition using approximate nearest neighbours search on large databases

Fast and accurate handwritten character recognition using approximate nearest neighbours search on large databases

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri