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Character recognition is one of the oldest applications of pattern recognition. Recognizing Hand-Written Characters (HWC) is an effortless task for humans, but for a computer it is a difficult job. Research in character recognition is very popular for various potential applications such as in banks, post offices, defense organizations, reading aid for the blind, library automation, language processing and multi-media design. Optical Character Recognition (OCR) is based on optical mechanism which consists of a machine to recognize scanned and digitized character automatically. Automatic recognition of handwritten text can be done either Offline or Online. Offline handwritten recognition is the task of recognizing the image of a hand written text, in contrast to Online recognition where the dynamic characteristics of the writing are available and recorded while the scriber is writing on a special screen with a pen/stylus made for this application. Zonal based feature extraction is used in the present proposed method. The character image is divided into predefined number of zones and a statistical feature is computed from each of these zones. Usually, this feature is based on the pixels contained in that zone. The gray values of the pixels in that selected zone are summed up to form a feature for that zone in that image. The features of all the zones in the image form a feature vector which is used for handwritten character recognition. Using this Zoning method the recognition accuracy is found to be 78%.

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