Scaled conjugate gradient algorithm in neural network based approach for handwritten text recognition

Abstract

Handwritten text and character recognition is a challenging task compared to recognition of handwritten numeral and computer printed text due to its large variety in nature. Neural Network based approach provides most reliable performance in handwritten character and text recognition but recognition performance depends upon some important factors like no of training samples, reliable features and no of features per character, training time, variety of handwriting etc. Important features from different types of handwriting are collected and are fed to the neural network for training. More no of features increases testing efficiency but it take longer time to converge the error curve. To reduce this training time effectively proper algorithm should be chosen so that the system provides best train and test efficiency in least possible time that is to provide the system fastest intelligence. In this paper we have used Scaled Conjugate Gradient Algorithm, a second order training algorithm for training of neural network. It provides faster training with excellent test efficiency. A scanned handwritten text is taken as input and character level segmentation is done. Some important and reliable features from each character are extracted and used as input to a neural network for training. When the error level reaches into a satisfactory level (10-12) weights are accepted for testing a test script. Finally a lexicon matching algorithm solves the minor misclassification problems. © 2011 Springer-Verlag Berlin Heidelberg.