There are different techniques that can be used to recognize handwritten digits and characters. Two techniques discussed in this paper are: Pattern Recognition and Artificial Neural Network. Both techniques are defined and different methods for each technique is also discussed. Bayesian Decision theory, Nearest Neighbor rule, and Linear Classification or Discrimination is types of methods for P...

Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. In this paper, we present DBN models trained for classification of handwritten digit characters. The structure of these models is partly inferred from the training data of each class of digit before performing parameter learning. Classification results are presented for the four described models.

In this paper, the cooperation of two feature families for handwritten digit recognition using a committee of Neural Network NN classifiers will be examined. Various cooperation schemes will be investigated and corresponding results will be presented. To improve the system reliability, we will upgrade the committee scheme using multistage classification based on rule-based and statistical coope...

The recognition of handwritten digit recognition has been a challenging problem among the researchers for few decades. This paper proposes a relative density feature extraction algorithm for recognizing unconstrained single connected handwritten numerals independent of the languages. The proposed method consists of four phases, namely, image enhancement (dilation), representation (zone based), ...

Thispaper proposes a novel approach for handwritten digit recognition system. The present paper extracts digit image features based on distance measure and derives an algorithm to classify the digit images. The distance measure can be performed on the thinned image. Thinning is the one of the preprocessing technique in image processing.The present paper mainly concentrated on an extraction of f...

For many years, researchers have studied high accuracy methods for recognizing the handwriting and achieved many significant improvements. However, an issue that has rarely been studied is the speed of these methods. Considering the computer hardware limitations, it is necessary for these methods to run in high speed. One of the methods to increase the processing speed is to use the computer pa...

Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach...

A new method for feature extraction based on FPGA (Field Programmable Gate Arrays) implementation is proposed in this paper. The specific application is offline Farsi handwritten digit recognition. The classification is based on a simple two layer MLP (Multi Layer Perceptron). This method of feature extraction is appropriate for FPGA implementation as it can be implemented only with addition an...