Sandhya Arora

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In this paper, we present an OCR for handwritten Devnagari characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character image while intersection features, chain(More)
Regular expressions are extremely useful, because they allow us to work with text in terms of patterns. They are considered the most sophisticated means of performing operations such as string searching, manipulation, validation, and formatting in all applications that deal with text data. Character recognition problem scenarios in sequence analysis that(More)
This paper deals with a new method for recognition of offline Handwritten noncompound Devnagari Characters in two stages. It uses two well known and established pattern recognition techniques: one using neural networks and the other one using minimum edit distance. Each of these techniques is applied on different sets of characters for recognition. In the(More)
In this paper a scheme for offline Handwritten Devnagari Character Recognition is proposed, which uses different feature extraction methodologies and recognition algorithms. The proposed system assumes no constraints in writing style or size. First the character is preprocessed and features namely : Chain code histogram and moment invariant features are(More)
Due to the limitations in single layer ANN researchers started losing interest in ANN during 1970s. Later on the development of multiple layer neural networks led to the development of many efficient techniques to recognize hand written/printed characters with great accuracies and also making the technology complex and costly. In this paper an effort was(More)
Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc. In this paper,(More)
In this paper a scheme for offline Handwritten Devnagari Character Recognition is proposed, which uses different feature extraction and recognition algorithms. The proposed system assumes no constraints in writing style, size or variations. First the character is preprocessed and features namely : Chain code histogram , four side views , shadow based are(More)
The paper presents a two stage classification approach for handwritten Devanagari characters. The first stage is using structural properties like shirorekha, spine in character and second stage exploits some intersection features of characters which are fed to a feedforward neural network. Simple histogram based method does not work for finding shirorekha,(More)
A novel, generic scheme for off-line handwritten English alphabets character images is proposed. The advantage of the technique is that it can be applied in a generic manner to different applications and is expected to perform better in uncertain and noisy environments. The recognition scheme is using a multilayer perceptron(MLP) neural networks. The system(More)
In this paper an automatic recognition system for isolated Handwritten Devanagari Numerals is proposed and compared the recognition rate with different classifier. We presented a feature extraction technique based on recursive subdivision of the character image so that the resulting sub-images at each iteration have balanced numbers of foreground pixels as(More)