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OCR System for Automating Answer Script Marks using Auto Resonance Network

Optical Character Recognition is one of the challenging areas in pattern recognition systems. It has gained a lot of importance in the fields of image processing and computer vision. In this paper, the handwritten digits on an evaluated answer booklet are taken for recognition by the neural network. Image acquisition, connected component analysis and contour detection methods are performed prior to the recognition process. The segmented handwritten digits are recognized through neural networks based on Auto Resonance Network as described in this paper. Auto Resonance Network can classify real valued multi-dimensional inputs and can have an adjustable threshold for each node in the network. The Modified National Institute of Standards and Technology database is used for training the network. An accuracy of 91% is achieved by using a single layer Auto Resonance Network.