Recognition of handwritten Kannada characters using hybrid features

The challenging task in the field of Document Image Analysis is automatic recognition of handwritten characters present in a scanned document. The recognition of characters in a document is achieved by Optical Character Recognition (OCR) system. In this paper, a hybrid feature extraction technique is proposed for recognizing handwritten Kannada characters. The proposed technique uses the local and global features as hybrid features. These features are extracted from each input image. 3600 samples are used as training data set to obtain consistent feature values. K-nearest neighbor classifier is used to classify the characters based on the feature values. The proposed method is tested on a dataset of 1200 samples and at present it shows an overall accuracy of 87.33%.