Handwritten Marathi Character Recognition Using R-HOG Feature

We use the Rectangle Histogram Oriented Gradient representation as the basis for extraction of features. These algorithms require a few simple arithmetic operations per image pixel which makes them suitable for real-time applications. Our dataset consists of 8000 samples each of 40 basic handwritten Marathi characters. Among these 10 samples of each character from different writers are collected. All sample images of handwritten Marathi characters are normalized to 20 × 20 pixel size. The description of the algorithm and experiment with our data set is presented in this paper. Experimental results using Support Vector Machines (SVM) and feed-forward Artificial Neural Network (FFANN) classification techniques are presented. Our results demonstrate high performance of these features when classified using feed-forward Artificial Neural network, classification.