Improving Word Spotting System Performance using Ensemble Classifier Combination Methods

The effective retrieval of information from scanned handwritten documents is becoming essential with the increasing amounts of digitized documents. Therefore, developing efficient means of analyzing and recognizing these documents is of significant interest. Among these methods is word spotting, which has recently become an active research area. Different ensemble classifiers have been successfully proposed to improve the performance of a pattern recognition or a word spotting system. In this paper, we propose an enhanced internal structure of the Arabic handwritten word spotting hierarchical classifier. In addition, we propose two ensemble classifier combination methods to improve the performance of closed lexicon word spotting systems. These methods are, 1) the improved score word matching method, and 2) score evaluation method. Both methods calculate a new score by utilizing the confidence values (scores) given by the combined classifiers. Support Vector Machines (SVM) and Regularized Discriminant Analysis (RDA) have been utilized to implement the proposed ensemble classifier. The proposed methods have been tested using the CENPARMI Arabic handwritten documents database, and the results show that combining classifiers has a significant improvement on word spotting systems. The precision rate increased by 4% and 17% respectively, when the improved score matching method and the score evaluation method have been used.