Vijay Kumar B - Academia.edu (original) (raw)
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Papers by Vijay Kumar B
Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP'04). IEEE International Conference on, May 17, 2004
Neural network based radial basis function networks (RBFN) and subspace projection approach have ... more Neural network based radial basis function networks (RBFN) and subspace projection approach have been employed to recognize printed Kannada characters. RBFNs are trained with wavelet features using K-means and subspace method is applied on normalized image. Use of structural features for disambiguating confused characters improved the recognition accuracy by 3% in case of subspace and by 1.6% using RBFN. Compared to subspace, a maximum recognition rate of 99.1% is achieved with RBFN using Haar wavelets and structural features.
5th International Workshop, DAS 2002 , Proceedings, Aug 19, 2002
This paper presents the design of a full fledged OCR system for printed Kannada text. The machine... more This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is dificult 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 conjuncts 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 Propagation 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.
Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP'04). IEEE International Conference on, May 17, 2004
Neural network based radial basis function networks (RBFN) and subspace projection approach have ... more Neural network based radial basis function networks (RBFN) and subspace projection approach have been employed to recognize printed Kannada characters. RBFNs are trained with wavelet features using K-means and subspace method is applied on normalized image. Use of structural features for disambiguating confused characters improved the recognition accuracy by 3% in case of subspace and by 1.6% using RBFN. Compared to subspace, a maximum recognition rate of 99.1% is achieved with RBFN using Haar wavelets and structural features.
5th International Workshop, DAS 2002 , Proceedings, Aug 19, 2002
This paper presents the design of a full fledged OCR system for printed Kannada text. The machine... more This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is dificult 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 conjuncts 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 Propagation 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.