In many computer vision applications for recognition or classification, outlier detection plays an important role as it affects the accuracy and reliability of the result. We propose a novel approach for outlier detection using Gaussian process classification. With this approach, the outlier detection can be integrated to the classification process, instead of being treated separately. Experime...

This paper describes a technique for the recognition of optical off-line handwritten Arabic (Indian) numerals using hidden Markov models (HMM). The success of HMM in speech recognition encouraged researchers to apply it to text recognition. In this work we did not follow the general trend of using sliding windows in the direction of the writing line to generate features. Instead we generated fe...

We use well-established results in biological vision to construct a novel vision model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear classifier on these features, our model is relatively simple yet outperforms other models on the same data set.

This paper describes the implementation of a Multilayer Perceptron Neural Network for handwritten digit recognition. The paper provides the knowledge about previously implemented techniques for same application and also provides their merits and demerits. In this paper Optimal Multilayer Perceptron.Neural network has been designed to reduce complexity of the circuit. Results are also stated to ...

It is herein proposed a handwritten digit recognition system which uses multiple feature extraction methods and classifier ensemble. The combination of the feature extraction methods is motivated by the observation that different feature extraction algorithms have a better discriminative power for some types of digits. Six features sets were extracted, two proposed by the authors and four publi...

Psychological data suggest that internal representations such as mental images can be used as templates in visual pattern recognition. But computational studies suggest that traditional template matching is insufficient for high-accuracy recognition of real-life patterns such as handwritten characters. Here we explore a model for visual pattern recognition that combines a template-matching and ...

Psychological data suggest that internal representations such as mental images can be used as templates in visual pattern recognition. But computational studies suggest that traditional template matching is insufficient for high-accuracy recognition of real-life patterns such as handwritten characters. Here we explore a model for visual pattern recognition that combines a template-matching and ...

In recent years, handwritten numeral classification has achieved remarkable attention in the field of computer vision. Handwritten numbers are difficult to recognize due different writing styles individuals. a multilingual country like India, negligible research attempts have been carried out for Gujarati numerals recognition using deep learning techniques compared other regional scripts. The d...

Novel pattern recognition techniques using multiple agents for the recognition of handwritten text are proposed in this paper. The concept of intelligent agents and innovative multi-agent architectures for pattern recognition tasks is introduced for combining and elaborating the classiication hypotheses of several classiiers. The architecture of a distributed digit-recognition system dispatchin...