Search Space Reduction for Farsi Printed Subwords Recognition by Simple Features, Feature Quantization and Fusion of Classifiers

Abstract- In this paper, a method is presented for search space reduction in Farsi Printed Sub words recognition. First 10 simple features from sub word are extracted. By using the concept of quantization, These features are quantized according to the interval changes of each feature in training data, and are converted to integers. A score is given to every class, using each feature and its distance to corresponding feature of each training sample. By applying all features, each class has a score per feature. A final score is obtained, by fusion of these scores using algebra operations, for each class. Search space is reduced using sorting of final scores and selection of some sub words with more scores. For fusion of scores, sum, prod, max, min and weighted sum operations are used. The weighted sum method, which Optimized weights are obtained by particle swarm optimization (PSO), has given the best response.