Optical Character Recognition Performance Analysis of SIF and LDF Based OCR
A performance analysis of character recognition by two different methods is presented, observing that the perceptron network converges faster, where as the BPN can handle the complex script recognition when the training set is enriched.
Abstract
The Optical Character Recognition (OCR) is becoming popular areas of research under pattern recognition and smart device applications. It requires the intelligence like human brain to recognize the various handwritten characters. Artificial Neural Network (ANN) is used to gather the information required to recognize the characters adaptively. This paper presents a performance analysis of character recognition by two different methods (1) compressed Lower Dimension Feature(LDF) matrix with a perceptron network, (2) Scale Invariant Feature (SIF) matrix with a Back Propagation Neural network (BPN). A GUI based OCR system is developed using Matlab. The results are shown for the English alphabets and numeric. This is observed that the perceptron network converges faster, where as the BPN can handle the complex script recognition when the training set is enriched.