Pradeep Kumar Jena, Charulata Palai, Lopamudra Sahoo and Anshupa Patel, National Institute of Science and Technology, India

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.

Character recognition, perceptron network, back-propagation neural network, scale invariant feature, low dimensional feature.