Devnagari Handwritten Character Recognition using LBG vector quantization with gradient masks

Several ways to generalize the Devanagari handwritten character recognition have been proposed in the past. In this paper, novel method of Handwritten Character Recognition with Shape and Texture features has been proposed. The method proposed here does not need the Preprocessing and Character Tokenization. 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 for texture feature 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 LBG codebooks are considered as shape texture feature vectors for handwritten character recognition. In all 40 variations of the character recognition method are proposed using five gradient operators and 9 code book sizes (from 4 to 1024). For experimentation the database having 72 images from 6 samples per character with 12 different characters is used. The crossover point of precision and recall is considered as performance comparison criteria for proposed character recognition techniques. The best performance is observed in LBG for codebook size 8 of Sobel operator and the next best is seen for codebook sizes 8 and 16 of Prewitt and Laplace gradient mask for feature extraction.