期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 46, 期 12, 页码 2825-2836出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2490165
关键词
Feature set evaluation; Institut fur Nachrichtentechnik/Ecole Nationale d'Ingenieurs de Tunis (IFN/ENIT); recurrent neural network (RNN); reconnaissance et indexation de donnees manuscrites et de fac similes (RIMES); system combination; word recognition
类别
资金
- Natural Sciences and Engineering Research Council of Canada [RGPIN 2014-04649]
- Social Sciences and Humanities Research Council of Canada [412-2010-1007]
The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than a straightforward comparison based on the recognition rate. In this paper, we propose a framework for feature set evaluation based on a collaborative setting. We use a weighted vote combination of recurrent neural network (RNN) classifiers, each trained with a particular feature set. This combination is modeled in a probabilistic framework as a mixture model and two methods for weight estimation are described. The main contribution of this paper is to quantify the importance of feature sets through the combination weights, which reflect their strength and complementarity. We chose the RNN classifier because of its state-of-the-art performance. Also, we provide the first feature set benchmark for this classifier. We evaluated several feature sets on the IFN/ENIT and RIMES databases of Arabic and Latin script, respectively. The resulting combination model is competitive with state-of-the-art systems.
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