1
Department of Computer engineering, Mobarakeh Branch, Islamic Azad University
2
Department of Computer engineering and IT, Payam Noor University
Abstract
Handwritten word recognition (HWR) is very important in document analysis and retrieval. In this paper, an off-line handwritten recognition system for Persian manuscript is introduced. For feature extraction, SIFT descriptors extracted densely from the block of word image and enriched by appending the normalized x and y coordinates and the scale they were extracted at. Linear discriminate analysis (LDA) is used for feature reduction. All words in the dictionary were hierarchically clustered by ISOCLUSE algorithm. In order to recognize the word images, multiple-class and two-class SVM classifiers methods were used. The experimental results showed a better performance in terms of speed and precision of two-class SVM method on the Iranshahr data set. The accuracy of proposed system by select 5 top cluster is shown 93.37% by 76.65% reduction of lexicon.
boroumand, S., & IranpourMobarakeh, M. (2017). Handwritten word recognition by new feature and lexicon reduction. Journal of Machine Vision and Image Processing, 4(2), 35-47.
MLA
somayeh boroumand; Majid IranpourMobarakeh. "Handwritten word recognition by new feature and lexicon reduction". Journal of Machine Vision and Image Processing, 4, 2, 2017, 35-47.
HARVARD
boroumand, S., IranpourMobarakeh, M. (2017). 'Handwritten word recognition by new feature and lexicon reduction', Journal of Machine Vision and Image Processing, 4(2), pp. 35-47.
VANCOUVER
boroumand, S., IranpourMobarakeh, M. Handwritten word recognition by new feature and lexicon reduction. Journal of Machine Vision and Image Processing, 2017; 4(2): 35-47.
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