holistic farsi handwritten word recognition using gradient features
نویسندگان
چکیده
in this paper we address the issue of recognizing farsi handwritten words. two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. these are directional and intensity gradient features. the feature vector extracted from each stripe is then coded using the self organizing map (som). in this method each word is modeled using the discrete hidden markov model (hmm). to evaluate the performance of the proposed method, farsa dataset has been used. the experimental results show that the proposed system, applying directional gradient features, has achieved the recognition rate of 69.07% and outperformed all other existing methods.
منابع مشابه
Holistic Farsi handwritten word recognition using gradient features
In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...
متن کاملHolistic Farsi handwritten word recognition using gradient features
In this paper we address the issue of recognizing Farsi handwritten words. Two types fo gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...
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عنوان ژورنال:
journal of ai and data miningناشر: shahrood university of technology
ISSN 2322-5211
دوره 4
شماره 1 2016
میزبانی شده توسط پلتفرم ابری doprax.com
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