In our research paper, we have implemented Multiclass Classification using Support Vector Machine (SVM). Pen Digit Recognition of Handwritten digit dataset is used for the purpose. One vs All approach has been applied using SVM to achieve multiclass classification. The same approach with different kernels has been analysed to select the right kernel. In this paper, we have found that selection ...

Abstract Due to the rapid development in artificial intelligence, classic Von Neumann architecture is no longer able meet demands of high-computing, high-storage, and high-bandwidth intelligence applications. To address this issue, paper proposes a handwritten digit recognition accelerator based on systolic array. First, 5 convolutional layer network built. Besides, The FPGA-based convolution e...

This paper addresses the identification of Arabic handwritten digits. In addition to digit identifiability, the paper presents digit recognition. The digit image is divided into grids based on the distribution of the black pixels in the image. Several types of features are extracted (viz. gradient, curvature, density, horizontal and vertical run lengths, stroke, and concavity features) from the...

Handwritten recognition is a popular problem which requires special Artificial Intelligent techniques for solving it. In this paper we use Genetic Programming (GP) for addressing the off-line variant of the handwritten digit recognition. We propose a new type of input representation for GP: histograms. This kind of representation is very simple and can be adapted very easily for the GP requirem...

We give our formulation for a ten class classification of handwritten Hindi digit recognition. Automatic Recognition of Handwritten Devnagri Numerals is a difficult task, because of the variability in writing style; pen used for writing and the color of handwriting, unlikely the printed character. Furthermore, Hindi Digit can be drawn in different sizes. Therefore, a robust offline Hindi handwr...

The work presented here involves the design of a Multi Layer Perceptron (MLP) based pattern classifier for recognition of handwritten Bangla digits using a 76 element feature vector. Bangla is the second most popular script and language in the Indian subcontinent and the fifth most popular language in the world. The feature set developed for representing handwritten Bangla numerals here include...

This work presents a system for pattern recognition that combines a self-organising unsupervised technique (via a Kohonen-type SOM) with a bayesian strategy in order to classify input patterns from a given probability distribution and, at the same time, detect ambiguous cases and explain answers. We apply the system to the recognition of handwritten digits. This proposal is intended as an impro...

The aim of this project was to evaluate the effectiveness of various types of classifiers in recognizing handwritten digits. It has been shown in pattern recognition that no single classifier performs the best for all pattern classification problems consistently. So the goal of the project was to experiment with different classifiers and combination methods and evaluate their performance in thi...

This paper introduces new database for Arabic handwritten words. The Arabic handwritten database (AHD/AMSH) represents a utility to facilitate the experiments of the character recognition algorithms. It contains three types of images: word, isolated character, and digit images. The AHD/AMSH can be used for baseline detection, characters segmentation, normalization, thinning, training and testin...