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Character recognition lies at the core of the discipline of pattern recognition where the aim is to represent a sequence of characters taken from an alphabet [ Document image analysis: a primer. Sadhana 27 (Part 1), 3–22]. Though many kinds of features have been developed and their test performances on standard database have been reported, there is still(More)
—In this paper, recognition system for totally unconstrained handwritten characters for south Indian language of Kannada is proposed. The proposed feature extraction technique is based on Fourier Transform and well known Principal Component Analysis (PCA). The system trains the appropriate frequency band images followed by PCA feature extraction scheme. For(More)
Separating text lines in handwritten documents remains a challenge because the text lines are often varying skewed and curved. In this paper, we propose a novel method for text line segmentation of unconstrained handwritten Kannada script. The proposed method consists of two phases. In the first phase, mathematical morphology technique is used to bridge the(More)
Handwritten character recognition is a difficult problem due to the great variations on writing styles, different size and orientation angle of the characters. In this paper, we propose an unconstrained handwritten Kannada character recognition based on the ridgelet transforms. Ridglets are a powerful instrument in catching and representing mono-dimensional(More)
In this paper, a Particle Swarm Optimization (PSO) method for tuning the parameters of multiscale retinex based color image enhancement is presented. The image enhancement using multiscale retinex scheme heavily depends on parameters such as Gaussian surround space constants, number of scales, gain and offset etc. Due to hard selection of these parameters,(More)