LBG Vector Quantization for Recognition of Handwritten Marathi Barakhadi

Handwritten character recognition has been studied a lot in the past and involves various problems due to many reasons. In this paper, novel method of Handwritten Marathi Barakhadi Character Recognition with Shape and Texture features has been proposed. The Shape features and Texture feature are more unique, so a novel technique based on combination of these is derived and proposed here. For extracting shape features standard gradient operator such as Robert, Prewitt, Sobel, Canny and Laplace are used and vector quantization technique. The gradient mask images of the character images are obtained and then LBG vector quantization algorithm is applied on these gradient images to get the codebooks of various sizes. These obtained codebooks are considered as shape texture feature vectors for handwritten character recognition. In all 45 variations of the character recognition method are proposed using five gradient operators and 9 code book sizes (from 4 to 1024).The database consists of 2100 images which consists of 35 consonants barakhadi written by 5 different people. The crossover point of precision and recall is considered as performance comparison criteria for proposed character recognition technique.