Handwritten Digit Recognition Using Machine Learning Algorithms

Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition includes in postal mail sorting, bank check processing, form data entry, etc. The heart of the problem lies within the ability to develop an efficient algorithm that can recognize hand written digits and which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning technique. The main objective of this paper is to ensure effective and reliable approaches for recognition of handwritten digits. Several machines learning algorithm namely, Multilayer Perceptron, Support Vector Machine, NaFDA5; Bayes, Bayes Net, Random Forest, J48 and Random Tree has been used for the recognition of digits using WEKA. The result of this paper shows that highest 90.37% accuracy has been obtained for Multilayer Perceptron

Design and Development of Handwritten Digit Recognition System Using Machine Learning

In any system ,the wide range of writing styles, handwritten digit recognition has proven to be a challenging task.The wide variety of writing styles has made it challenging to read handwritten numerals.. Numerous research have demonstrated the superior performance of neural networks in the classification of data. The major goal of this work is to compare various existing classification models in order to give effective and trustworthy methods for handwritten numeral recognition. As training sets, a total of 45,000 images with a pixel size of 2828 were employed. The original image was compared to the training sets and images. We also found that the classifier's accuracy increases with the amount of training data. The study's conclusion shows that K-NN maintains an accuracy of 96.7% as opposed to 96.8% while processing data at a rate nearly ten times faster than a neural network