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Combination of Multiple Image Features along with KNN Classifier for Classification of Marathi Barakhadi

Character recognition is an emerging area for research in terms of different languages spoken all over the world and the associated writing of them. India itself has 11 different scripts and each script has its own subscripts. This diversity gives a wide scope for research out of which devnagari script has been chosen for studying its problems and solutions for those problems. Devnagari has marathi as one of its complicated language which has barakhadi as its characteristic part. A lot of researchers have worked on determining the marathi characters more efficiently, problem listed during this work are the styles of writing, strokes, aspect ratio etc. Data mining is evolving in various fields such as satellite images, medical images, object specific images etc. This paper discusses a new system that combines the Image processing methods along with the data mining classification algorithm which is a new trend called as image mining. The proposed technique applies data acquisition, pre-processing steps such as grayscale conversion, edge detection, binarization and feature extraction methods such as hu moments and GLCM feature extraction from image processing and extracted features are given to Data mining KNN classification algorithm for getting the classification results. The Database used is handwritten barakhadi of 3024 images of 36 barakhadi consonants and 12 vowels written by 7 different people from different age groups. The Proposed system will efficiently and effectively classify the character into its exact category and will reflect a very high performance as compared to others for this hybrid system which is never done before.