Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
Handwritten character recognition of popular south Indian scripts
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Experiences of integration and performance testing of multilingual OCR for printed Indian scripts
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Recognition of Kannada characters extracted from scene images
Proceeding of the workshop on Document Analysis and Recognition
On performance analysis of end-to-end OCR systems of Indic scripts
Proceeding of the workshop on Document Analysis and Recognition
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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.