Recognition of Handwritten Numerals Using a Combined Classifier with Hybrid Features

Abstract

AbstractOff-line handwritten numeral recognition is a very difficult task. It is hard to achieve high recognition results using a single set of features and a single classifier, since handwritten numerals contain many pattern variations which mostly depend upon individual writing styles. In this paper, we propose a recognition system using hybrid features and a combined classifier. To improve recognition rate, we select mutually beneficial features such as directional features, crossing point features and mesh features, and create three new hybrid feature sets from them. These feature sets hold the local and global characteristics of input numeral images. We also implement a combined classifier from three neural network classifiers to achieve a high recognition rate, using fuzzy integral for multiple network fusion. In order to verify the performance of the proposed recognition system, experiments with the unconstrained handwritten numeral database of Concordia University, Canada were performed, producing a recognition rate of 97.85%.KeywordsFeature VectorRecognition RateFeature Extraction MethodRecognition ResultFuzzy MeasureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.