OcrV1, PascalFrancis, Checkpoint, bibRecord, 000607

Machine recognition of printed Kannada text

Identifieur interne : 000607 ( PascalFrancis/Checkpoint ); précédent : 000606; suivant : 000608

Machine recognition of printed Kannada text

Auteurs : B. Vijay Kumar [Inde] ; A. G. Ramakrishnan [Inde]

Source :

Lecture notes in computer science [ 0302-9743 ] ; 2002.

RBID : Pascal:03-0248638

Descripteurs français

Pascal (Inist)
- Transformation ondelette, Reconnaissance caractère, Reconnaissance optique caractère, Fonction Haar, Reconnaissance forme, Fonction base radiale, Segmentation image, Réseau neuronal, Transformation discrète, Traitement image, Transformation cosinus discrète, Classification forme, Karnataka.

English descriptors

KwdEn :
- Character recognition, Discrete cosine transforms, Discrete transformation, Haar function, Image processing, Image segmentation, Karnataka, Neural network, Optical character recognition, Pattern classification, Pattern recognition, Radial basis function, Wavelet transformation.

Abstract

This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.

Affiliations:

Inde

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Pascal:03-0248638

Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.</div>
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Machine recognition of printed Kannada text

Machine recognition of printed Kannada text

Source :

Descripteurs français

English descriptors

Abstract

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

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri