Abstract—In OCR applications, the feature extraction methods used to recognize document images play an important role. The feature extraction methods may be statistical, structural or transforms and series expansion. The structural features are very difficult to extract particularly in handwritten applications. The structural behavior of the strokes existing in the handwritten expressions can be estimated through statistical methods too. In this paper, a feature extraction method is proposed that measures the distribution of black and white pixels representing various strokes in a character image by computing the weights on all the four corners on a pixel due to its neighboring black pixels. The feature is named as Neighborhood Pixels Weights (NPW). Its recognition performance is compared with some feature extraction methods, which have been generally used as secondary feature extraction methods for the recognition of many scripts in literature, on noisy and non-noisy handwritten character images. The experiments have been conducted using 17000 Devanagari handwritten character images. The experiments have been made using two classifiers i.e. Probabilistic Neural Network and k-Nearest Neighbor Classifier. NPW feature is better as compared to other features, studied here, in noisy and noise-less situation.
Index Terms—Devanagari script, Hand-printed recognition, NPW (Neighborhood pixels weights), Random noise, Statistical features, Weighted map.
Dr. Satish Kumar is with Panjab University and is serving at its Regional Centre, Muktsar, Punjab, India ; email: email@example.com;
Cite: Satish Kumar, "Neighborhood Pixels Weights-A New Feature Extractor," International Journal of Computer Theory and Engineering vol. 2, no. 1, pp. 69-77, 2010.