A. P. Shivaprasad

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A variety of different scripts are used in writing languages throughout the world. In a multiscript, multilingual environment, it is essential to know the script used in writing a document before an appropriate character recognition and document analysis algorithm can be chosen. In view of this, several methods for automatic script identification have been(More)
As the supply voltage to a standard CMOS op-amp is reduced, the input common mode range and the output swing get reduced drastically. Special biasing circuits have to be used to raise them up to rail-to-rail supply voltage. Three low voltage op-amps with new biasing circuits have been proposed in this paper and their performance evaluated. The op-amp design(More)
A variety of different scripts are used in writing languages throughout the world. In a multiscript, multilingual environment, it is essential to know the script used in writing a document before an appropriate character recognition and document analysis algorithm can be chosen. In view of this, several methods for automatic script identification have been(More)
Recognition rate of handwritten character is still limited around 90 percent due to the presence of large variation of shape, scale and format in hand written characters. A sophisticated hand written character recognition system demands a better feature extraction technique that would take care of such variation of hand writing. In this paper, we propose a(More)
In this paper we are extracting feature of handwritten and ISM printed characters of devanagri script. we are extracting Gradient feature of the devanagari script ,for that we are using two operators i.e. Sobel and Robert operator respectively . Here we are computing gradient in 8,12,16,32 directions and getting different feature vectors respectively. We(More)
In this paper a binary decision tree, based on Neural Networks, Support Vector Machine and K-Nearest Neighbor is employed and presented for recognition of Persian handwritten isolated digits and characters. In the proposed method, a part of the training data is divided into two clustersusing a clustering algorithm, and this process continues until each(More)