A class-modular generalized learning vector quantization (GLVQ) ensemble method with outlier learning for handwritten digit recognition is proposed. A GLVQ classifier is one of discriminative methods. Though discriminative classifiers have remarkable ability to solve character recognition problems, they are poor at outlier resistance. To overcome this problem, a GLVQ classifier trained with bot...

A method for recognition of Persian handwritten digits based on characterization loci and mixture of experts is proposed. This method utilizes the characterization loci, as the main feature. In the classification stage of our proposed method the mixture of experts are applied. This recognition method is applied to Farsi hand-written digits in the HODA database. The experimental results support ...

An efficient method for increasing the generalization capacity of neural character recognition is presented. The network uses a biologically inspired architecture for feature extraction and character classification. The numerical methods used are, however, optimized for use on massively parallel array processors. The method for training set construction, when applied to handwritten digit recogn...

-A new segmentation method for segmenting connected handwritten digit strings is presented. Unlike traditional methods where segmentation points are uniquely determined to cut the piece of stroke joining the connected numerals, our approach is one of identifying regions which serve as potential segmentation points. The regions are identified by a thorough analysis of the trajectory of strokes.....

The objective of the paper is to recognize handwritten samples of Roman numerals using Tesseract open source Optical Character Recognition (OCR) engine. Tesseract is trained with data samples of different persons to generate one user-independent language model, representing the handwritten Roman digit-set. The system is trained with 1226 digit samples collected form the different users. The per...

This paper deals with an optical character recognition (OCR) system of handwritten digit, with the use of neural networks (MLP multilayer perceptron). And a method of extraction of characteristics based on the digit form, this method is tested on the MNIST handwritten isolated digit database (60000 images in learning and 10000 images in test). This work has achieved approximately 80% of success...

Recognition of handwritten numbers in any language is one of the most important issues attracted many researchers and this is because of the increasing importance of the issue in today's world and in applications such as automatic detection of mail addresses or identification of numbers of bank checks. Given the great interclass diversities and much extra-class similarities between some Persian...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...