Handwritten Kannada Character Recognition using Wavelet Transform and Structural Features

The frontier area of research in the field of pattern recognition and image processing is handwritten character recognition. This leads to a great demand for OCR system containing handwritten documents. In order to recognize the text present in a document, an Optical Character Recognition (OCR) system is developed. In this paper, OCR system for handwritten Kannada characters and numerals is developed which involves several phases such as preprocessing, feature extraction and classification. Preprocessing includes the techniques that are suitable to convert the input image into an acceptable form for feature extraction. The main aim of this paper is to propose an efficient feature extraction and classification techniques. Suitable features are extracted as structural features and wavelet transform is employed for extracting global features. Artificial neural network classifier is used for recognizing the handwritten Kannada characters and numerals. The proposed method is experimented on 4800 images of handwritten Kannada characters and obtained an average accuracy of 91.00%. Also, the proposed method is experimented on 1000 images of handwritten Kannada numerals and obtained an average accuracy of 97.60%.