Traditional systems of handwriting recognition have relied on handcrafted features and a
large amount of prior knowledge. Training an Optical character recognition (OCR) system …

Existing techniques for hand-written digit recognition (HDR) rely heavily on the hand-coded
key points and requires prior knowledge. Training an efficient HDR network with these …

The choice of pattern classifier and the technique used to extract the features are the main
factors to judge the recognition accuracy and the capability of an Optical Character …

Handwriting digit recognition is a computer technology that allows it to accept and decipher
sound transcribed input from various sources, including paper reports, contact screens, and …

Arabic and Hindi handwritten numeral detection and classification is one of the most popular
fields in the automation research. It has many applications in different fields. Automatic …

Character recognition is one of the most interesting and challenging research areas in the
field of image processing. The recognition rate of handwritten character is still limited due to …

This work is focused on improving the character recognition capability of feed-forward back-
propagation neural network by using one, two and three hidden layers and the modified …

The aim of this work is to judge the efficiency of Multi Layer Perceptron (MLP) and Radial
Basis Function (RBF) neural network classifiers for performing the task of cursive …

Precise and accurate automatic recognition of decimal numbers is essential for many
applications. Many methods have been used in the literature for this purpose. In this paper …

Optical Character Recognition (OCR), a system for automatic recognition, is used in a variety
of application sectors to convert text or images into editable data. With the distinct outline …