State of The Art in Handwritten Digit Recognition Pooja Agrawal Department of Computer Science, SVITS, Indore, Madhya Pradesh, INDIA Prof. Anand Rajavat Department of Computer Science, SVITS, Indore, Madhya Pradesh, INDIA RGPV/SVITS Indore Sanwer Road, Gram Baroli, Alwasa, Indore, Madhya Pradesh, INDIA ______________________________________________________________________________________ Abstra...

The cursive nature of Persian alphabet, and the complex and convoluted rules regarding this script cause major challenges to segmentation as well as recognition of Persian words. We propose a new segmentation algorithm for the main stroke of online Persian handwritten words. Using this segmentation, we present a perturbation method which is used to generate artificial samples from handwritten w...

MNIST database serves for comparison of different methods of handwritten digit recognition. There are many data related to different classifier recognition rates among which our neural classifier had the second place [1] (recognition rate 99.21%). At present we develop improvements of neural network structure and algorithms of handwritten digit recognition. Improved classifier has recognition r...

In computer vision the most difficult task is to recognize the handwritten digit. Since the last decade the handwritten digit recognition is gaining more and more fame because of its potential range of applications like bank cheque analysis, recognizing postal addresses on postal cards, etc. Handwritten digit recognition plays a very vital role in day to day life, like in a form of recording of...

This paper presents the results of Persian handwritten word recognition based on Mixture of Experts technique. In the basic form of ME the problem space is automatically divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our proposed model, we used Mixture of Experts Multi Layered Perceptrons with Momentum term, in the classification ...

This paper introduces a novel deep learning architecture, named DIGITNET, and large-scale digit dataset, DIDA, to detect recognize handwritten digits in historical document images written the nineteen century. To generate DIDA are collected from 100,000 Swedish images, which were by different priests with handwriting styles. dataset contains three sub-datasets including single digit, bounding b...

An automatic feature generation method for handwritten digit recognition is described. Two different evaluation measures, orthogonality and information, are used to guide the search for features. The features are used in a backpropagation trained neural network. Classification rates compare favorably with results published in a survey of high-performance handwritten digit recognition systems. T...